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1
Joshi, Miland. "Applications of generalized additive models." Thesis, University of Warwick, 2011. http://wrap.warwick.ac.uk/47759/.
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Main Purpose The study is primarily a contribution to a question of strategy rather than the development of a new method. It explores the circumstances in which the use of generalized additive models can be recommended. It is thus a contribution to answering the question "When is it a good idea (or not so good an idea) to use GAMs?" Content Following an introductory exposition in which they are compared to generalized linear models, subsequent chapters deal with evidence that could support possible recommendations: 1. A survey of recent studies, in which GAMs have been used and recommended, regarded with greater reserve, or compared to other methods. 2. Original case studies in which the applicability of GAMs is investigated, namely: (a) Receiver operating characteristic curves used in medical diagnostic testing, the associated diagnostic likelihood ratios, and the modelling of the risk score. (b) A study of a possible heat wave effect on mortality in London. (c) Shorter studies, including a study of factors influencing the length of stay in hospital in Queensland, Australia, and a simulation study. 3. Diagnostics, looking in particular at concurvity, and the problems of defining and detecting it. The study ends with recommendations for the use of GAMs, and possible areas for further research. The appendices include a glossary, technical appendices and R code for computations involved in the project.
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Pya, Natalya. "Additive models with shape constraints." Thesis, University of Bath, 2010. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.527433.
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In many practical situations when analyzing a dependence of one or more explanatory variables on a response variable it is essential to assume that the relationship of interest obeys certain shape constraints, such as monotonicity or monotonicity and convexity/concavity. In this thesis a new approach to shape preserving smoothing within generalized additive models has been developed. In contrast with previous quadratic programming based methods, the project develops intermediate rank penalized smoothers with shape constrained restrictions based on re-parameterized B-splines and penalties based on the P-spline ideas of Eilers and Marx (1996). Smoothing under monotonicity constraints and monotonicity together with convexity/concavity for univariate smooths; and smoothing of bivariate functions with monotonicity restrictions on both covariates and on only one of them are considered. The proposed shape constrained smoothing has been incorporated into generalized additive models with a mixture of unconstrained and shape restricted smooth terms (mono-GAM). A fitting procedure for mono-GAM is developed. Since a major challenge of any flexible regression method is its implementation in a computationally efficient and stable manner, issues such as convergence, rank deficiency of the working model matrix, initialization, and others have been thoroughly dealt with. A question about the limiting posterior distribution of the model parameters is solved, which allows us to construct Bayesian confidence intervals of the mono-GAM smooth terms by means of the delta method. The performance of these confidence intervals is examined by assessing realized coverage probabilities using simulation studies. The proposed modelling approach has been implemented in an R package monogam. The model setup is the same as in mgcv(gam) with the addition of shape constrained smooths. In order to be consistent with the unconstrained GAM, the package provides key functions similar to those associated with mgcv(gam). Performance and timing comparisons of mono-GAM with other alternative methods has been undertaken. The simulation studies show that the new method has practical advantages over the alternatives considered. Applications of mono-GAM to various data sets are presented which demonstrate its ability to model many practical situations.
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Utami, Zuliana Sri. "Penalized regression methods with application to generalized linear models, generalized additive models, and smoothing." Thesis, University of Essex, 2017. http://repository.essex.ac.uk/20908/.
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Recently, penalized regression has been used for dealing problems which found in maximum likelihood estimation such as correlated parameters and a large number of predictors. The main issues in this regression is how to select the optimal model. In this thesis, Schall’s algorithm is proposed as an automatic selection of weight of penalty. The algorithm has two steps. First, the coefficient estimates are obtained with an arbitrary penalty weight. Second, an estimate of penalty weight λ can be calculated by the ratio of the variance of error and the variance of coefficient. The iteration is continued from step one until an estimate of penalty weight converge. The computational cost is minimized because the optimal weight of penalty could be obtained within a small number of iterations. In this thesis, Schall’s algorithm is investigated for ridge regression, lasso regression and two-dimensional histogram smoothing. The proposed algorithm are applied to real data sets and simulation data sets. In addition, a new algorithm for lasso regression is proposed. The performance of results of the algorithm was almost comparable in all applications. Schall’s algorithm can be an efficient algorithm for selection of weight of penalty.
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Marra, Giampiero. "Some problems in model specification and inference for generalized additive models." Thesis, University of Bath, 2010. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.527788.
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Regression models describingthe dependence between a univariate response and a set of covariates play a fundamental role in statistics. In the last two decades, a tremendous effort has been made in developing flexible regression techniques such as generalized additive models(GAMs) with the aim of modelling the expected value of a response variable as a sum of smooth unspecified functions of predictors. Many nonparametric regression methodologies exist includinglocal-weighted regressionand smoothing splines. Here the focus is on penalized regression spline methods which can be viewed as a generalization of smoothing splines with a more flexible choice of bases and penalties. This thesis addresses three issues. First, the problem of model misspecification is treated by extending the instrumental variable approach to the GAM context. Second, we study the theoretical and empirical properties of the confidence intervals for the smooth component functions of a GAM. Third, we consider the problem of variable selection within this flexible class of models. All results are supported by theoretical arguments and extensive simulation experiments which shed light on the practical performance of the methods discussed in this thesis.
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Feng, Zhenghui. "Estimation and selection in additive and generalized linear models." HKBU Institutional Repository, 2012. https://repository.hkbu.edu.hk/etd_ra/1435.
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Hercz, Daniel. "Flexible modeling with generalized additive models and generalized linear mixed models: comprehensive simulation and case studies." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=114300.
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This thesis compares GAMs and GLMMs in the context of modeling nonlinear curves. The study contains a comprehensive simulation and a few real life data analyses. The simulation uses thousands of generated datasets to compare and contrast the two models' (and linear models as a benchmark) fit, extent of nonlinearity, and shape of the resulting curve. The data analyses extend the results of the simulation to GLMM/GAM curves of lung function with measures of smoking as the independent variable. An additional and larger real life data analysis with dichotomous outcomes rounds out the study and allow for more representative results.Cette these compare des GAM et GLMM dans le cadre de la modélisation des courbes non-linéaires. L'étude comprend une simulation complète et quelques analyses réelles. La simulation utilise des milliers de 'datasets' générés pour comparer forme entres les deux modèles (et les modèles linéaires comme point de repère), l'étendue de la non-linéarité, et la forme de la courbe obtenue. Les analyses d'étendre les résultats de la simulation à courbes de la fonction pulmonaire avec de GLMM / GAM avec mesures du tabagisme (la variable indépendante). Un autre analyse réelle avec les résultats dichotomiques complète l'étude et que les résultats soient plus représentatifs.
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Koehn, Sebastian. "Generalized additive models in the context of shipping economics." Thesis, University of Leicester, 2009. http://hdl.handle.net/2381/4172.
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This thesis addresses three current issues in maritime economics by the application of semi-parametric estimations within a generalized additive model framework. First, this thesis shows that there are vessel and contract specific differences in time charter rates for dry bulk vessels. The literature on microeconomic factors of time charter rates could show the emergence of a two-tier tanker market during the post-OPA90 period. However, previous results do not allow for any safe conclusions about the existence of a two-tier dry bulk market. This thesis extends the results of previous research by showing that a good part of the variation in physical time charter rates is due to microeconomic factors. It empirically proves the existence of a two-tier dry-bulk market. Controlling for a variety of contract specific effects as well as vessel specific factors the presented model quantifies quality induced differences in physical dry bulk charter rates. Second, the literature on the formation of ship prices focuses exclusively on rather homogeneous shipping segments, such as tankers and dry bulk carriers. Due to the comparatively low number of sales and the complexity of the ships, vessel valuation in highly specialised and small sectors, such as chemical tankers, is a much more challenging task. The empirical results of this thesis confirm the findings in recent literature that ship valuation is a non-linear function of size, age and market conditions, whilst other parameters that are particular to the chemicals market also play a significant role. The third topic addresses the recent increase in operational expenses of merchant vessels (opex). The available literature cannot explain the development nor provides information on vessel individual level. This thesis considers a quantitative model of opex that is particularly successful in explaining the variation in opex across vessels of different type, size, age and specification. The results confirm that differences in opex are due to the behaviour of a vessel's operator and to regulatory requirements. Furthermore, it shows that there are significant differences in opex due to earnings and employment status of a vessel.
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Pan, Yiyang. "A robust fit for generalized partial linear partial additive models." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/44647.
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In regression studies, semi-parametric models provide both flexibility and interpretability.
In this thesis, we focus on a robust model fitting algorithm for a family of semi-parametric models – the Generalized Partial Linear Partial Addi- tive Models (GAPLMs), which is a hybrid of the widely-used Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs). The traditional model fitting algorithms are mainly based on likelihood proce- dures. However, the resulting fits can be severely distorted by the presence of a small portion of atypical observations (also known as “outliers”), which deviate from the assumed model. Furthermore, the traditional model diag- nostic methods might also fail to detect outliers. In order to systematically solve these problems, we develop a robust model fitting algorithm which is resistant to the effect of outliers. Our method combines the backfitting algorithm and the generalized Speckman estimator to fit the “partial linear partial additive” styled models. Instead of using the likelihood-based weights and adjusted response from the generalized local scoring algorithm (GLSA), we apply the robust weights and adjusted response derived form the robust quasi-likelihood proposed by Cantoni and Ronchetti (2001). We also extend previous methods by proposing a model prediction algorithm for GAPLMs.
To compare our robust method with the non-robust one given by the R function gam::gam(), which uses the backfitting algorithm and the GLSA, we report the results of a simulation study. The simulation results show that our robust fit can effectively resist the damage of outliers and it performs similarly to non-robust fit in clean datasets. Moreover, our robust algorithm is observed to be helpful in identifying outliers, by comparing the fitted values with the observed response variable.
In the end, we apply our method to analyze the global phytoplankton data. We interpret the outliers reported by our robust fit with an exploratory analysis and we see some interesting patterns of those outliers in the dataset. We believe our result can provide more information for the relative research.
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De, Zan Martina <1994>. "ExplainableAI: on explaining forest of decision trees by using generalized additive models." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/18604.
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In recent years, decision support systems have become more and more perva-
sive in our society, playing an important role in our everyday life. But these
systems, often called black-box models, are extremely complex and it may be
impossible to understand or explain how they work in a human interpretable
way. This lack of explainability is an issue: ethically because we have to be
sure that our system is fair and reasonable; practically because people tend
to trust more what they understand.
However, substituting black-box model with a more interpretable one in the
process of decision making may be impossible: interpretable model may not
work as well as the original one or training data may be no longer available.
In this thesis we focus on forests of decision trees, which are particular cases
of black-box models. If fact, trees are interpretable models, but forests are
composed by thousand of trees that cooperate to take decisions, making the
final model too complex to comprehend its behavior.
In this work we show that Generalized Additive Models (GAMs) can be
used to explain forests of decision trees with a good level of accuracy. In
fact, GAMs are linear combination of single-features or pair-features mod-
els, called shape functions. Since shape functions can be only one- or two-
dimensional functions, they can be easily visualized and interpreted by user.
At the same time, shape functions can be arbitrarily complex, making GAMs
as powerful as other more complex models.
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VITRANO, Angela. "Modelling Spatio-Temporal Elephant Movement Data: a Generalized Additive Mixed Models Framework." Doctoral thesis, Università degli Studi di Palermo, 2014. http://hdl.handle.net/10447/90988.
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This thesis focuses on understanding how environmental factors influence
elephant movement and in investigating the spatio-temporal patterns.
The thesis analyses movement data of some African elephants (Loxodonta
Africana) living in the Kruger National Park and its associated private reserves of South Africa. Due to heterogeneity among elephants, and nonlinear relationships between elephant movement and environmental variables,
Generalized Additive Mixed Models (GAMMs) were employed. Results
showed delayed effects of rainfall and temperature and particular trends in
time and space.
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Cao, Jiguo. "Generalized profiling method and the applications to adaptive penalized smoothing, generalized semiparametric additive models and estimating differential equations." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102483.
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Many statistical models involve three distinct groups of variables: local or nuisance parameters, global or structural parameters, and complexity parameters. In this thesis, we introduce the generalized profiling method to estimate these statistical models, which treats one group of parameters as an explicit or implicit function of other parameters. The dimensionality of the parameter space is reduced, and the optimization surface becomes smoother. The Newton-Raphson algorithm is applied to estimate these three distinct groups of parameters in three levels of optimization, with the gradients and Hessian matrices written out analytically by the Implicit Function Theorem it' necessary and allowing for different criteria for each level of optimization. Moreover, variances of global parameters are estimated by the Delta method and include the variation coming from complexity parameters. We also propose three applications of the generalized profiling method.First, penalized smoothing is extended by allowing for a functional smoothing parameter, which is adaptive to the geometry of the underlying curve, which is called adaptive penalized smoothing. In the first level of optimization, the smooth ing coefficients are local parameters, estimated by minimizing sum of squared errors, conditional on the functional smoothing parameter. In the second level, the functional smoothing parameter is a complexity parameter, estimated by minimizing generalized cross-validation (GCV), treating the smoothing coefficients as explicit functions of the functional smoothing parameter. Adaptive penalized smoothing is shown to obtain better estimates for fitting functions and their derivatives.
Next, the generalized semiparametric additive models are estimated by three levels of optimization, allowing response variables in any kind of distribution. In the first level, the nonparametric functional parameters are nuisance parameters, estimated by maximizing the regularized likelihood function, conditional on the linear coefficients and the smoothing parameter. In the second level, the linear coefficients are structural parameters, estimated by maximizing the likelihood function with the nonparametric functional parameters treated as implicit functions of linear coefficients and the smoothing parameter. In the third level, the smoothing parameter is a complexity parameter, estimated by minimizing the approximated GCV with the linear coefficients treated as implicit functions of the smoothing parameter. This method is applied to estimate the generalized semiparametric additive model for the effect of air pollution on the public health.
Finally, parameters in differential equations (DE's) are estimated from noisy data with the generalized profiling method. In the first level of optimization, fitting functions are estimated to approximate DE solutions by penalized smoothing with the penalty term defined by DE's, fixing values of DE parameters. In the second level of optimization, DE parameters are estimated by weighted sum of squared errors, with the smoothing coefficients treated as an implicit function of DE parameters. The effects of the smoothing parameter on DE parameter estimates are explored and the optimization criteria for smoothing parameter selection are discussed. The method is applied to fit the predator-prey dynamic model to biological data, to estimate DE parameters in the HIV dynamic model from clinical trials, and to explore dynamic models for thermal decomposition of alpha-Pinene.
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Nian, Gaowei. "A score test of homogeneity in generalized additive models for zero-inflated count data." Kansas State University, 2014. http://hdl.handle.net/2097/18230.
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Master of ScienceDepartment of Statistics
Wei-Wen Hsu
Zero-Inflated Poisson (ZIP) models are often used to analyze the count data with excess zeros. In the ZIP model, the Poisson mean and the mixing weight are often assumed to depend on covariates through regression technique. In other words, the effect of covariates on Poisson mean or the mixing weight is specified using a proper link function coupled with a linear predictor which is simply a linear combination of unknown regression coefficients and covariates. However, in practice, this predictor may not be linear in regression parameters but curvilinear or nonlinear. Under such situation, a more general and flexible approach should be considered. One popular method in the literature is Zero-Inflated Generalized Additive Models (ZIGAM) which extends the zero-inflated models to incorporate the use of Generalized Additive Models (GAM). These models can accommodate the nonlinear predictor in the link function. For ZIGAM, it is also of interest to conduct inferences for the mixing weight, particularly evaluating whether the mixing weight equals to zero. Many methodologies have been proposed to examine this question, but all of them are developed under classical zero-inflated models rather than ZIGAM. In this report, we propose a generalized score test to evaluate whether the mixing weight is equal to zero under the framework of ZIGAM with Poisson model. Technically, the proposed score test is developed based on a novel transformation for the mixing weight coupled with proportional constraints on ZIGAM, where it assumes that the smooth components of covariates in both the Poisson mean and the mixing weight have proportional relationships. An intensive simulation study indicates that the proposed score test outperforms the other existing tests when the mixing weight and the Poisson mean truly involve a nonlinear predictor. The recreational fisheries data from the Marine Recreational Information Program (MRIP) survey conducted by National Oceanic and Atmospheric Administration (NOAA) are used to illustrate the proposed methodology.
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Agharkar, Amal. "Model Validation and Comparative Performance Evaluation of MOVES/CALINE4 and Generalized Additive Models for Near-Road Black Carbon Prediction." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1490350586489513.
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Li, Zheyuan. "Generalized additive models for large datasets : spatial-temporal modelling of the UK's Daily Black Smoke (1961-2005)." Thesis, University of Bath, 2019. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.767604.
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The UK Black Smoke monitoring network has produced daily particulate air pollution data from a network of up to 1200 monitoring stations over several decades, resulting in 10 million measurements in total. Spatial-temporal modelling of the data is desirable for accurate trend/seasonality estimation and mapping and to provide daily exposure estimates for epidemiological cohort studies. Generalized additive models offer one way to do this if we can deal with the data volume and model size. This thesis will develop computation method for estimating generalized additive models having $O(10^4)$ coefficients and $O(10^8)$ observations. The strategy combines 3 elements: (i) fine scale discretization of covariates, (ii) an efficient approach to restricted likelihood optimization, that avoids computation of numerically awkward log determinant terms and (iii) restricted likelihood optimization algorithms that make good use of numerical linear algebra methods with high performance and good parallel scaling on mordern multi-core machines. The new method enables us to estimate spatial-temporal models for daily Black Smoke data over the last four decades at a daily resolution which had once been infeasible. A spatial-temporal dataset of daily Black Smoke is also produced on a grid of 5km x 5km resolution. Our prediction is shown to suffer from little extrapolation and bias.
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Miftahuddin, Miftahuddin. "Modelling sea surface temperature using generalized additive models for location scale and shape by boosting with autocorrelation." Thesis, University of Essex, 2016. http://repository.essex.ac.uk/16501/.
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Sea surface temperature (SST) is one of many important parameters that influence the climate system of the earth. Modelling of and prediction from the SST data are challenging due to the fact that gaps in the data lead to incomplete information over time. Generalized additive models by boosting with location scale and shape (gamboostLSS) can be applied to overcome this problem. Moreover, they also deal with sparsity, irregular peaks, and autocorrelation in the data. We propose in this thesis extended gamboostLSS models by considering time autocorrelation. In our experiments, we initially used 1231 daily observations in the period between November 2006 and September 2012. The data is then further extended from three different moored buoys. The data consisting of the SST as the response from buoys in the Indian Ocean and the air temperature (in Celsius), humidity (in percentage) and rainfall (in millimetre) covariates are considered from land stations in Sumatra Island. Removing autocorrelation with an AR(1) model has a large impact on global and local model fitting. GamboostLSS-AR(1) models are an advanced technique for removing autocorrelation. We also computed marginal prediction interval with autocorrelation (MPIAR(1)) of the model. MPI-AR(1) of the gamboost LSS-AR(1) model can be used to predict the missing data in various gaps and to obtain a prediction interval of submodels. The MPI-AR(1) that is applied to different buoys indicated that gamboost LSS-AR(1) model fitting is better than MPI by gamboost LSS model with and without transformation of rainfall. The MPI-AR(1) is more flexible to follow the pattern of the SST data fitting. Our proposed gamboost LSS-AR(1) models are more flexible, interpretable and capable to handle missing data, as well as to deal with high dimensional data and capture complex data structures.
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Kirichenko, L., I. Ivanisenko, and T. Radivilova. "Investigation of Multifractal Properties of Additive Data Stream." Thesis, 1 th IEEE International Conference on Data Stream Mining & Processing, 2016. http://openarchive.nure.ua/handle/document/3810.
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The work presents results of a numerical
study of fractal characteristics of multifractal stream at
addition of stream, which does not have multifractal
properties. They showed that the generalized Hurst
exponent of total stream tends to one of original
multifractal stream with increase in signal/noise ratio.
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Holanda, Amanda Amorim. "Modelos lineares parciais aditivos generalizados com suavização por meio de P-splines." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-31052018-113859/.
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Neste trabalho apresentamos os modelos lineares parciais generalizados com uma variável explicativa contínua tratada de forma não paramétrica e os modelos lineares parciais aditivos generalizados com no mínimo duas variáveis explicativas contínuas tratadas de tal forma. São utilizados os P-splines para descrever a relação da variável resposta com as variáveis explicativas contínuas. Sendo assim, as funções de verossimilhança penalizadas, as funções escore penalizadas e as matrizes de informação de Fisher penalizadas são desenvolvidas para a obtenção das estimativas de máxima verossimilhança penalizadas por meio da combinação do algoritmo backfitting (Gauss-Seidel) e do processo iterativo escore de Fisher para os dois tipos de modelo. Em seguida, são apresentados procedimentos para a estimação do parâmetro de suavização, bem como dos graus de liberdade efetivos. Por fim, com o objetivo de ilustração, os modelos propostos são ajustados à conjuntos de dados reais.In this work we present the generalized partial linear models with one continuous explanatory variable treated nonparametrically and the generalized additive partial linear models with at least two continuous explanatory variables treated in such a way. The P-splines are used to describe the relationship among the response and the continuous explanatory variables. Then, the penalized likelihood functions, penalized score functions and penalized Fisher information matrices are derived to obtain the penalized maximum likelihood estimators by the combination of the backfitting (Gauss-Seidel) algorithm and the Fisher escoring iterative method for the two types of model. In addition, we present ways to estimate the smoothing parameter as well as the effective degrees of freedom. Finally, for the purpose of illustration, the proposed models are fitted to real data sets.
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Nakamura, Luiz Ricardo. "Advances on the Birnbaum-Saunders distribution." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-30092016-171320/.
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The Birnbaum-Saunders (BS) distribution is the most popular model used to describe lifetime process under fatigue. Throughout the years, this distribution has received a wide ranging of applications, demanding some more flexible extensions to solve more complex problems. One of the most well-known extensions of the BS distribution is the generalized Birnbaum- Saunders (GBS) family of distributions that includes the Birnbaum-Saunders special-case (BSSC) and the Birnbaum-Saunders generalized t (BSGT) models as special cases. Although the BS-SC distribution was previously developed in the literature, it was never deeply studied and hence, in this thesis, we provide a full Bayesian study and develop a tool to generate random numbers from this distribution. Further, we develop a very flexible regression model, that admits different degrees of skewness and kurtosis, based on the BSGT distribution using the generalized additive models for location, scale and shape (GAMLSS) framework. We also introduce a new extension of the BS distribution called the Birnbaum-Saunders power (BSP) family of distributions, which contains several special or limiting cases already published in the literature, including the GBS family. The main feature of the new family is that it can produce both unimodal and bimodal shapes depending on its parameter values. We also introduce this new family of distributions into the GAMLSS framework, in order to model any or all the parameters of the distribution using parametric linear and/or nonparametric smooth functions of explanatory variables. Throughout this thesis we present five different applications in real data sets in order to illustrate the developed theoretical results.A distribuição Birnbaum-Saunders (BS) é o modelo mais popular utilizado para descrever processos de fadiga. Ao longo dos anos, essa distribuição vem recebendo aplicações nas mais diversas áreas, demandando assim algumas extensões mais flexíveis para resolver problemas mais complexos. Uma das extensões mais conhecidas na literatura é a família de distribuições Birnbaum-Saunders generalizada (GBS), que inclui as distribuições Birnbaum-Saunders casoespecial (BS-SC) e Birnbaum-Saunders t generalizada (BSGT) como modelos especiais. Embora a distribuição BS-SC tenha sido previamente desenvolvida na literatura, nunca foi estudada mais profundamente e, assim, nesta tese, um estudo bayesiano é desenvolvido acerca da mesma além de um novo gerador de números aleatórios dessa distribuição ser apresentado. Adicionalmente, um modelo de regressão baseado na distribuição BSGT é desenvolvido utilizando-se os modelos aditivos generalizados para locação, escala e forma (GAMLSS), os quais apresentam grande flexibilidade tanto para a assimetria como para a curtose. Uma nova extensão da distribuição BS também é apresentada, denominada família de distribuições Birnbaum-Saunders potência (BSP), que contém inúmeros casos especiais ou limites já publicados na literatura, incluindo a família GBS. A principal característica desta nova família é que ela é capaz de produzir formas tanto uni como bimodais dependendo do valor de seus parâmetros. Esta nova família também é introduzida na estrutura dos modelos GAMLSS para fornecer uma ferramenta capaz de modelar todos os parâmetros da distribuição como funções lineares e/ou não-lineares suavizadas de variáveis explicativas. Ao longo desta tese são apresentadas cinco diferentes aplicações em conjuntos de dados reais para ilustrar os resultados teóricos obtidos.
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Thomas, Nicole. "Validation of Criteria Used to Predict Warfarin Dosing Decisions." BYU ScholarsArchive, 2004. https://scholarsarchive.byu.edu/etd/40.
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People at risk for blood clots are often treated with anticoagulants, warfarin is such an anticoagulant. The dose's effect is measured by comparing the time for blood to clot to a control time called an INR value. Previous anticoagulant studies have addressed agreement between fingerstick (POC) devices and the standard laboratory, however these studies rely on mathematical formulas as criteria for clinical evaluations, i.e. clinical evaluation vs. precision and bias. Fourteen such criteria were found in the literature. There exists little consistency among these criteria for assessing clinical agreement, furthermore whether these methods of assessing agreement are reasonable estimates of clinical decision-making is unknown and has yet to be validated. One previous study compared actual clinical agreement by having two physicians indicate a dosing decision based on patient history and INR values. This analysis attempts to justify previously used mathematical criteria for clinical agreement. Generalized additive models with smoothing spline estimates were calculated for each of the 14 criteria and compared to the smoothing spline estimate for the method using actual physician decisions (considered the "gold standard"). The area between the criteria method spline and the gold standard method spline served as the comparison, using bootstrapping for statistical inference. Although some of the criteria methods performed better than others, none of them matched the gold standard. This stresses the need for clinical assessment of devices.
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Betnér, Staffan. "Trends in Forest Soil Acidity : A GAM Based Approach with Application on Swedish Forest Soil Inventory Data." Thesis, Uppsala universitet, Statistiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-352392.
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The acidification of soils has been a continuous process since at least the beginning of the 20th century. Therefore, an inquiry of how and when the soil pH levels have changed is relevant to gain better understanding of this process. The aim of this thesis is to study the average national soil pH level over time in Sweden and the local spatial differences within Sweden over time. With data from the Swedish National Forest Inventory, soil pH surfaces are estimated for each surveyed year together with the national average soil pH using a generalized additive modeling approach with one model for each pair of consecutive years. A decreasing trend in average national level soil pH was found together with some very weak evidence of year-to-year differences in the spatial structure of soil pH.
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Sun, Peng. "Semiparametric Bayesian Approach using Weighted Dirichlet Process Mixture For Finance Statistical Models." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/78189.
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Cross, Cheryl L. "Predictive Habitat Models for Four Cetaceans in the Mid-Atlantic Bight." NSUWorks, 2010. http://nsuworks.nova.edu/occ_stuetd/221.
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This study focuses on the habitats of cetaceans in the Mid-Atlantic Bight, a region characterized by bathymetric diversity and the presence of distinct water masses (i.e. the shelf water, slope water, and Gulf Stream). The combination of these features contributes to the hydrographic complexity of the area, which furthermore influences biological productivity and potential prey available for cetaceans. The collection of cetacean sighting data together with physical oceanographic data can be used to examine cetacean habitat associations. Cetacean habitat modeling is a mechanism for predicting cetacean distribution patterns based on environmental variables such as bathymetric and physical properties, and for exploring the potential ecological implications that contribute to cetacean spatial distributions. We can advance conservation efforts of cetacean populations by expanding our knowledge of their habitats and distribution.
Generalized additive models (GAMs) were developed to predict the spatial distribution patterns of sperm whales (Physeter macrocephalus), pilot whales (Globicephala spp.), bottlenose dolphins (Tursiops truncatus), and Atlantic spotted dolphins (Stenella frontalis) based on significant physical parameters along the continental shelf-break region in the Mid-Atlantic Bight. Data implemented in the GAMs were collected in the summer of 2006 aboard the NOAA R/V Gordon Gunter. These included visual cetacean survey data collected along with physical data at depth via expendable bathythermograph (XBT), and conductivity-temperature-depth (CTD) instrumentation. Additionally, continual surface data were collected via the ship’s flow through sensor system. Interpolations of physical data were created from collected point data using the inverse distant weighted method (IDW) to estimate the spatial distribution of physical data within the area of interest. Interpolated physical data, as well as bathymetric (bottom depth and slope) data were extracted to overlaid cetacean sightings, so that each sighting had an associated value for nine potentially significant physical habitat parameters.
A grid containing 5x5 km grid cells was created over the study area and cetacean sightings along with the values for each associated habitat parameter were summarized in each grid cell. Redundant parameters were reduced, resulting in a full model containing temperature at 50 m depth, mixed layer depth, bottom depth, slope, surface temperature, and surface salinity. GAMs were fit for each species based on these six potentially significant parameters. The resultant fit models for each species predicted the number of individuals per km2 based on a unique combination of environmental parameters. Spatial prediction grids were created based on the significant habitat parameters for each species to illustrate the GAM outputs and to indicate predicted regions of high density. Predictions were consistent with observed sightings. Sperm whale distribution was predicted by a combination of depth, sea surface temperature, and sea surface salinity. The model for pilot whales included bottom slope, and temperature at 50 m depth. It also indicated that mixed layer depth, bottom depth and surface salinity contributed to group size. Similarly, temperature at 50 m depth was significant for Atlantic spotted dolphins. Predicted bottlenose dolphin distribution was determined by a combination of bottom slope, surface salinity, and temperature at 50 m depth, with mixed layer depth contributing to group size.
Distribution is most likely a sign of prey availability and ecological implications can be drawn from the habitat parameters associated with each species. For example, regions of high slope can indicate zones of upwelling, enhanced vertical mixing and prey availability throughout the water column. Furthermore, surface temperature and salinity can be indicative of patchy zones of productivity where potential prey aggregations occur.
The benefits of these models is that collected point data can be used to expand our knowledge of potential cetacean “hotspots” based on associations with physical parameters. Data collection for abundance estimates, higher resolution studies, and future habitat surveys can be adjusted based on these model predictions. Furthermore, predictive habitat models can be used to establish Marine Protected Areas with boundaries that adapt to dynamic oceanographic features reflecting potential cetacean mobility. This can be valuable for the advancement of cetacean conservation efforts and to limit potential vessel and fisheries interactions with cetaceans, which may pose a threat to the sustainability of cetacean populations.
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Belitz, Christiane. "Model Selection in Generalised Structured Additive Regression Models." Diss., lmu, 2007. http://nbn-resolving.de/urn:nbn:de:bvb:19-78896.
Full text
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Roza, Daiane Leite da. "Fatores associados à gravidez adolescente no estado de Minas Gerais, Brasil: análise espaço-temporal." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/17/17139/tde-01022016-154531/.
Full textAbstract:
O objetivo deste trabalho foi descrever as associações entre os percentuais de gravidez na adolescência e indicadores socioeconômicos e de responsabilidade social dos municípios do estado de Minas Gerais, sudeste do Brasil, no ano de 2000 a 2010. Trata-se de um estudo ecológico, utilizando dados do Sistema de Informações sobre Nascidos Vivos (SINASC). O percentual de nascidos vivos de mães adolescentes para cada município foi calculado segundo o quociente entre o número de nascidos vivos de mães com idade entre 10 e 19 anos e o número total de nascidos vivos registrados no ano de 2000 a 2010. Modelos bayesianos e modelos aditivos generalizados foram utilizados para a obtenção de percentuais de gravidez adolescente ajustados por efeitos espaciais e para avaliar as possíveis associações com os indicadores socioeconômicos e de responsabilidade social. Os percentuais brutos de gravidez adolescente em relação ao total de nascidos vivos nos municípios de Minas Gerais no ano de 2010 variaram de 0 a 46,4%, com uma mediana de 19,6%. O primeiro e o terceiro quartis são, respectivemente, 15,6% e 23,1%. O estudo evidenciou uma estreita relação entre a gravidez na adolescência e indicadores econômicos e sociais. Os percentuais de gravidez adolescente se mostraram maiores nos municípios com menor tamanho populacional, menores valores do Índice de Desenvolvimento Humano e menores valores de outros indicadores de desenvolvimento. A forte relação entre os percentuais de gravidez adolescente e os indicadores sociais e econômicos sugerem que a gravidez adolescente é muito mais um problema social que biológico. Os programas e as ações devem ir muito além de educação sexual e informações sobre métodos preventivos de saúde.The objective of this study was to describe associations between pregnancy rates in adolescence and socio-economic and social responsibility indicators in the municipalities of the State of Minas Gerais, Southeast of Brazil, in the year of 2010- 2010. This is an ecological study using data from the Brazilian Live Birth Information System (SINASC). The percentage of live births to adolescent mothers for each municipality was calculated based on the quotient between number of born alive infants of mothers aged 10-19 years old and total number of live births in the year of 2000-2010. Bayesian models and generalized additive model were used to obtain the percentages of adolescence pregnancy adjusted for spatial effects and to assess possible associations with socio-economic and social responsibility indicators. The crude percentage of adolescence pregnancy for the total number of live births in the municipalities of Minas Gerais in 2010 ranged from 0 to 46.4%, with median percentage being 19.6% and the first and third quartiles being 15.6% and 23.1%, respectively. This study has demonstrated a close relationship between adolescent pregnancy and socio-economic indicators. Live births to adolescent mothers percentages were found to be higher in municipalities with low population density, low human development index, and other low development indicators. The strong relationship between adolescence pregnancy percentages and socio-economic indicators suggests that adolescent pregnancy is more a social than a biological problem. Therefore, programs and actions should go beyond sexual education and information on preventive health methods.
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Valente, Ana Margarida dos Santos. "Red and roe deer densities and distribution in Northeastern Portugal." Master's thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/14929.
Full textAbstract:
Mestrado em Ecologia AplicadaMonitoring ungulates is a major challenge to perform management strategies, either back in the 70’s to enable their conservation that lead to their great recovery, as to manage their actual expansion. Their current wide range distribution and high den-sities across Europe promotes damages in ecosystems that need to be handled based on scientific knowledge. In Portugal ungulate monitoring and ecology is still in an early stage, however efforts have been made to gather valid information on north-eastern ungulate populations. In this work density of red and roe deer were estimated coupling line transects to perform pellet group counts with a distance sampling ap-proach. The density of red deer estimated for Montesinho Natural Park (MNP) was 3.05 ind./100 ha (95% CI: 2.05 – 4.54), splitted in two sub-areas: Serra de Mon-tesinho (SM) with 1.23 ind./100 ha and Lombada National Hunting Area (LNHA) with 5.23 ind./100 ha. Roe deer densities were estimated with recourse to a spatial methodology recently developed, the Density Surface Models (DSMs – with a dis-tance sampling framework), which enables the assessment of the relationships be-tween animal’s density and spatial variables selected according to species ecological requirements. As well roe deer densities were estimated for MNP and Serra da Nogueira (SN) with a global density of 3.01 ind./100 ha (95% CI: 2.34 – 3.87): SM with 3.74 ind./100 ha, LNHA with 1.59 ind./100 ha and SN with 3.62 ind./100ha. Furthermore this approach enables the drawing of an abundance distribution map across the study area, especially useful when communicating results to wildlife man-agers. Roe deer densities showed to increase as distance to roads increased, while surprisingly shown an increase as distance to human populations decreased. As ex-pected, cover areas shown its importance for roe deer, a prey species for Iberian wolf. The spatial analysis confirmed that DSMs represent a good approach to estimate ungulate densities, and should be encouraged in future works. Future studies are mandatory to assess red and roe deer ecological requirements and evaluate trends over the years, in order to stablish management plans to handle the damages caused by these species.
A monitorização de ungulados constitui um passo essencial no desenvolvimento de estratégias de gestão. Desde os anos 70, quando os esforços para a conservação e gestão destas espécies permitiram a sua expansão, até aos dias de hoje, a gestão de habitats e espécies tem tido um papel central na ecologia. A ampla distribuição atual dos ungulados selvagens e as suas elevadas abundâncias na Europa provocam danos nos ecossistemas, que têm que ser geridos com base em conhecimento científico. Em Portugal a monitorização das populações de ungulados, bem como o estudo da sua ecologia encontra-se ainda numa fase inicial, no entanto têm sido desenvolvidos avanços significativos no conhecimento das populações de ungulados no nordeste transmontano. Neste trabalho foram estimadas densidades de veado e corço através do distance sampling aplicado a transectos lineares com contagem de excrementos. A densidade de veado no Parque Natural de Montesinho (PNM) foi de 3.05 ind./100 ha (IC a 95%: 2.05 – 4.54) dividido em duas sub-áreas: Serra de Montesinho (SM) com 1.23 ind./100 ha e Lombada National Hunting Area (LNHA) com 5.23 ind./100 ha. As densidades de corço foram estimadas com recurso a uma metodologia espa-cial desenvolvida recentemente, os Density Surface Models (DSMs – baseados no distance sampling) que permitem relacionar as densidades populacionais com as va-riáveis espaciais escolhidas de acordo com a ecologia da espécie. As densidades de corço foram estimadas para o PNM e para a Serra da Nogueira (SN) apresentando uma densidade global de 3.01 ind./100 ha (IC a 95%: 2.34 – 3.87): SM com 3.74 ind./100 ha, LNHA com 1.59 ind./100 ha e SN com 3.62 ind./100 ha. Adicional-mente este método permite construir um mapa de distribuição de abundâncias ao longo da área de estudo, o que é particularmente útil ao comunicar os resultados aos responsáveis pela gestão das áreas protegidas. Do ponto de vista ecológico, as den-sidades de corço aumentaram à medida que a distância às estradas aumentou, mos-trando, surpreendentemente, uma redução na densidade à medida que a distância às populações humanas aumentou. Tratando-se de uma espécie-presa do lobo-ibérico, as áreas de abrigo revelaram-se importantes para o corço. A análise espacial confir-mou que os DSMs são um método robusto para estimar densidades de ungulados e analisar a sua ecologia. Estudos futuros são essenciais para identificar as necessida-des ecológicas do veado e do corço, bem como para avaliar oscilações nas densida-des ao longo dos anos, para que seja possível estabelecer planos de gestão que per-mitam mitigar os danos causados por estas espécies.
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Mugodo, James, and n/a. "Plant species rarity and data restriction influence the prediction success of species distribution models." University of Canberra. Resource, Environmental & Heritage Sciences, 2002. http://erl.canberra.edu.au./public/adt-AUC20050530.112801.
Full textAbstract:
There is a growing need for accurate distribution data for both common and rare plant
species for conservation planning and ecological research purposes. A database of more than
500 observations for nine tree species with different ecological and geographical
distributions and a range of frequencies of occurrence in south-eastern New South Wales
(Australia) was used to compare the predictive performance of logistic regression models,
generalised additive models (GAMs) and classification tree models (CTMs) using different
data restriction regimes and several model-building strategies. Environmental variables
(mean annual rainfall, mean summer rainfall, mean winter rainfall, mean annual temperature,
mean maximum summer temperature, mean minimum winter temperature, mean daily
radiation, mean daily summer radiation, mean daily June radiation, lithology and
topography) were used to model the distribution of each of the plant species in the study
area.
Model predictive performance was measured as the area under the curve of a receiver
operating characteristic (ROC) plot. The initial predictive performance of logistic regression
models and generalised additive models (GAMs) using unrestricted, temperature restricted,
major gradient restricted and climatic domain restricted data gave results that were contrary
to current practice in species distribution modelling. Although climatic domain restriction
has been used in other studies, it was found to produce models that had the lowest predictive
performance. The performance of domain restricted models was significantly (p = 0.007)
inferior to the performance of major gradient restricted models when the predictions of the
models were confined to the climatic domain of the species. Furthermore, the effect of data
restriction on model predictive performance was found to depend on the species as shown by
a significant interaction between species and data restriction treatment (p = 0.013). As found
in other studies however, the predictive performance of GAM was significantly (p = 0.003)
better than that of logistic regression. The superiority of GAM over logistic regression was
unaffected by different data restriction regimes and was not significantly different within
species.
The logistic regression models used in the initial performance comparisons were based on
models developed using the forward selection procedure in a rigorous-fitting model-building
framework that was designed to produce parsimonious models. The rigorous-fitting modelbuilding
framework involved testing for the significant reduction in model deviance (p =
0.05) and significance of the parameter estimates (p = 0.05). The size of the parameter
estimates and their standard errors were inspected because large estimates and/or standard
errors are an indication of model degradation from overfilling or effecls such as mullicollinearily.
For additional variables to be included in a model, they had to contribule
significantly (p = 0.025) to the model prediclive performance. An attempt to improve the
performance of species distribution models using logistic regression models in a rigorousfitting
model-building framework, the backward elimination procedure was employed for
model selection, bul it yielded models with reduced performance.
A liberal-filling model-building framework that used significant model deviance reduction at
p = 0.05 (low significance models) and 0.00001 (high significance models) levels as the
major criterion for variable selection was employed for the development of logistic
regression models using the forward selection and backward elimination procedures. Liberal
filling yielded models that had a significantly greater predictive performance than the
rigorous-fitting logistic regression models (p = 0.0006). The predictive performance of the
former models was comparable to that of GAM and classification tree models (CTMs). The
low significance liberal-filling models had a much larger number of variables than the high
significance liberal-fitting models, but with no significant increase in predictive
performance. To develop liberal-filling CTMs, the tree shrinking program in S-PLUS was
used to produce a number of trees of differenl sizes (subtrees) by optimally reducing the size
of a full CTM for a given species. The 10-fold cross-validated model deviance for the
subtrees was plotted against the size of the subtree as a means of selecting an appropriate
tree size. In contrast to liberal-fitting logistic regression, liberal-fitting CTMs had poor predictive performance.
Species geographical range and species prevalence within the study area were used to
categorise the tree species into different distributional forms. These were then used, to
compare the effect of plant species rarity on the predictive performance of logistic regression
models, GAMs and CTMs. The distributional forms included restricted and rare (RR)
species (Eucalyptus paliformis and Eucalyptus kybeanensis), restricted and common (RC)
species (Eucalyptus delegatensis, Eucryphia moorei and Eucalyptus fraxinoides),
widespread and rare (WR) species (Eucalyptus data) and widespread and common (WC)
species (Eucalyptus sieberi, Eucalyptus pauciflora and Eucalyptus fastigata). There were
significant differences (p = 0.076) in predictive performance among the distributional forms
for the logistic regression and GAM. The predictive performance for the WR distributional
form was significantly lower than the performance for the other plant species distributional
forms. The predictive performance for the RC and RR distributional forms was significantly
greater than the performance for the WC distributional form. The trend in model predictive
performance among plant species distributional forms was similar for CTMs except that the
CTMs had poor predictive performance for the RR distributional form.
This study shows the importance of data restriction to model predictive performance with
major gradient data restriction being recommended for consistently high performance. Given
the appropriate model selection strategy, logistic regression, GAM and CTM have similar
predictive performance. Logistic regression requires a high significance liberal-fitting
strategy to both maximise its predictive performance and to select a relatively small model
that could be useful for framing future ecological hypotheses about the distribution of
individual plant species. The results for the modelling of plant species for conservation
purposes were encouraging since logistic regression and GAM performed well for the
restricted and rare species, which are usually of greater conservation concern.
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Zhang, Tianyang. "Partly parametric generalized additive model." Diss., University of Iowa, 2010. https://ir.uiowa.edu/etd/913.
Full textAbstract:
In many scientific studies, the response variable bears a generalized nonlinear regression relationship with a certain covariate of interest, which may, however, be confounded by other covariates with unknown functional form. We propose a new class of models, the partly parametric generalized additive model (PPGAM) for doing generalized nonlinear regression with the confounding covariate effects adjusted nonparametrically. To avoid the curse of dimensionality, the PPGAM specifies that, conditional on the covariates, the response distribution belongs to the exponential family with the mean linked to an additive predictor comprising a nonlinear parametric function that is of main interest, plus additive, smooth functions of other covariates. The PPGAM extends both the generalized additive model (GAM) and the generalized nonlinear regression model. We propose to estimate a PPGAM by the method of penalized likelihood. We derive some asymptotic properties of the penalized likelihood estimator, including consistency and asymptotic normality of the parametric estimator of the nonlinear regression component. We propose a model selection criterion for the PPGAM, which resembles the BIC. We illustrate the new methodologies by simulations and real applications. We have developed an R package PPGAM that implements the methodologies expounded herein.
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Ok, Meltem. "Evaluation Of The Demersal Fish Assemblages Of The Northeastern Levant Sea." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12615068/index.pdf.
Full textAbstract:
Ecosystem-level changes have taken place in the Mediterranean Sea over the last decades due to both anthropogenic interferences and natural perturbations. Compared to the western Mediterranean Sea, influences of these factors especially on flora and fauna characteristics are much more dramatic and intense in the eastern part, particularly in the northeastern Levant Sea where the study area is located. In this study, life history traits of some core species (both native and immigrant) occupying the continental shelf of the northeastern Levant Sea were studied in this changing ecosystem to improve limited ecological understanding of the demersal fish assemblages of the northeastern Levant Sea. For this purpose, the annual patterns in allocation and utilization of energy in demersal fish species, temporal and bathymetrical trends in fish distribution with respect to biological requirements of the species and strategies adapted by the species in growth, reproduction and energy storage were investigated by examining growth parameters, biological indices and abundance and biomass variations. Influences of environmental variables on spatiotemporal distribution and biological characteristic of Mullus barbatus were also explored by generalized additive models. Biological data were collected at monthly intervals between May 2007 and May 2010 by trawl sampling while sample collection of environmental variables (temperature and salinity) was performed from December 2008 to May 2010. Results of this study reveal that the components of the demersal fish assemblage in the region fulfill their biological activities within a short period of time when the highest productivity is reached in the area. Moreover, results indicate that within this short period of time, some native components of the demersal fish assemblages studied (Mullus barbatus and Pagellus erythrinus) exhibit strategies such as fast growth, early maturation, short reproduction season, secondary spawners to cope with the environmental peculiarities. On the other hand, the successful exotic colonizers develop strategies as well but these successful immigrants also use time (Lagocephalus suezensis) and space (depth) (Upeneus pori) slot that the native species avoid. In some of the species examined (Mullus barbatus and Lagocephalus suezensis), growth is fast, sexual maturity is early, reproduction period is short, and reproduction potential is high. With the peculiar environmental condition, these life history traits are attributed to the &ldquor-strategy&rdquo
of the species. In this study, generalized additive models of Mullus barbatus explain 81.5 % variations in Gonadosomatic Index (GSI), 55.2 % in Hepatosomatic Index (HSI) and 43.9 % in Condition Factor (K). The time component in the GAM model captures the same cyclic pattern observed in GSI of Mullus barbatus. Besides, The GAM results suggest that the highest GSI values associated with the bottom water temperature are between 18 &ndash
19 °
C while the partial effect of bottom salinity is at 38.7 psu. A positive effect of depth on GSI of the species starts after 60 meters depth and increasing trend continues until 125 meters depth and then decreases. The HSI results are almost identical to GSI outputs indicating that the effects of the parameters concerned act in a similar manner. The results of the GAM models failed to explain influence of environmental parameters on vertical and seasonal distribution of adult Mullus barbatus. However 83.5 % variances were explained in distribution of juveniles. The salinity and temperature have the highest impact on the distribution of juveniles among the parameters evaluated. The results indicate that the occurrence of Atlantic Water in the area has a positive influence on M. barbatus, particularly on the recruits through either by its low salinity or by another factor associated with this water mass. The vertical distribution range are set by the high temperatures (>
27 °
C) at the shallow depths during summer and the low temperatures on the shelf break zone (<
16 °
C). A comparison of vertical abundance distribution of Mullus barbatus and the vertical temperature variations indicate that the species may tolerate up to 27 °
C and then individuals move to the deeper depths so that to the cooler waters when the temperature exceeds their tolerance limit. As well as the life history traits adopted by the species, there are some other factors providing advantages to the species. The fisheries regulations, particularly the time limits applied in the area are in favor of the species especially of pre-recruits. In the study area the pre-recruitment phase and summer YOY aggregations in shallow waters of most species studied in this thesis take place during a time when the fishing season is closed.
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Viau, Elizabeth C. "Fish Communities on Natural and Artificial Reefs in the Eastern Gulf of Mexico." Scholar Commons, 2019. https://scholarcommons.usf.edu/etd/7981.
Full textAbstract:
Artificial reefs have been deployed throughout the world’s oceans to act as habitat and fishing enhancement tools. To expand current research on the role of artificial reefs in the marine community, ordination and multivariate regression methods were used here to analyze survey data of natural and artificial reefs. The reefs, located in the Northern Gulf of Mexico (NGOM) and on the West Florida Shelf (WFS), had been previously surveyed from 2004 to 2015 using remote operated vehicle and stationary video techniques. This study tested the hypothesis that similar functional roles are accounted for at both natural and artificial reef sites even if species composition varies. Secondly, it examines the role of environment and fisheries in determining the assemblages. Artificial reefs tended to host communities that were as biodiverse as natural reefs, although not necessarily composed of the same species. Results of an ordination confirmed that as the classification was broadened from the level of species, to family, to functional group, the assemblages on each reef type (natural vs. artificial and NGOM vs WFS) appeared more similar. Dominant groups were present at all levels of classification and included the families Lutjanidae and Carangidae, as well as functional groups Red Snapper and Small Reef Fish. Both natural and artificial reefs tended to be dominated by one of the following: Lutjanidae, Carangidae, or Small Reef Fish, although a continuous gradient was found across the extremes of natural versus artificial reefs. Generalized Additive Models were developed to examine the influence of reef type, location, environment and fishing intensity covariates. Results indicated that for both natural and artificial reefs, the abundance of families and functional groups can be influenced by environmental factors. In both cases, there is strong spatial autocorrelation suggesting connectivity with neighboring reefs.
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Lucenteforte, E. "IL FUMO DI SIGARETTA E IL RISCHIO DI TUMORE DEL PANCREAS: DIVERSI APPROCCI DI ANALISI IN UNA POOLED-ANALYSIS." Doctoral thesis, Università degli Studi di Milano, 2012. http://hdl.handle.net/2434/168456.
Full textAbstract:
Background: To evaluate the dose–response relationship between cigarette smoking and pancreatic cancer risk and to examine the effects of temporal variables.
Aim: The aim of my PhD thesis is to explore the effect of selected smoking variable (including dose, duration and time since quitting) on the risk of pancreatic cancer using the two-stage and the multilevel analysis, and to compare these results with those obtained using the “standard” aggregate analysis. Moreover, generalized additive models were used to analyse the relation between smoking and pancreatic cancer risk without any data constriction.
Methods: Data from 12 case–control studies, within the International Pancreatic Cancer Case–Control Consortium (PanC4) and including 6507 pancreatic cases and 12 890 controls, were analyzed. In the aggregate analysis, smoking variables were categorized and odds ratios (ORs), and corresponding 95 confidence intervals (CIs), were calculated using logistic regression models adjusted for selected covariates (sex, age, ethnicity, education, body mass index, alcohol consumption, and history of diabetes and of pancreatitits) and study center. In the two-stage analysis, smoking variables were categorized, and summary ORs were calculated pooling study-specific ORs using random-effects models. Study-specific ORs were calculated using logistic regression models adjusted for the same covariates used for adjustment in the aggregate analysis. In the multilevel analysis, smoking variables were categorized and summary ORs were calculated using hierarchical models with two levels of hierarchy, considering study center as level 1, and subject as level 2. At level 2, models were adjusted the same covariates used for adjustment in the aggregate analysis. Finally, smoking variables were considered as continuous and generalized additive logistic regression were used to explore the nonlinear effects.
Results - aggregate analysis: Compared with never smokers, the OR was 1.46 (95% confidence interval [CI] 1.36–1.57) for former smokers and 2.00 (96% CI : 1.83-2.19) for current cigarette smokers, with a significant increasing trend in risk with increasing number of cigarettes among current smokers (OR = 3.17 for more than 40 cigarettes per day, P for trend <0.0001). Risk increased in relation to duration of cigarette smoking up to 40 years of smoking (OR = 2.02). Moreover, compared to current cigarette smokers, the risk decreased with increasing time since cigarette cessation, in fact the OR being 0.46 after 30 years.
Results - two-stage analysis: Point estimates were similar to those obtained in the aggregate analysis, whereas interval ones were larger. There was substantial heterogeneity among studies, except for current smokers of less than 10 cigarette per day (p-value=0.0952) or more than 40 cigarette per day (p-value=0.2815), for current smokers who smoked for 20-<30 years (p-value=0.2309), and for ex-smokers who time since quitting 10-<15 years (p-value=0.0756) or 15-<20 years (p-value=0.0739).
Results - multilevel analysis: Point ant interval estimates were similar to those obtained in the aggregate analysis. As regard number of cigarettes, risks were stronger among females, subjects with less than 65 years, and among subjects drinking 0-1 drinks of alcohol per day. No differences were found for duration and time since quitting.
Though significant increasing trend in risk with increasing number of cigarettes, duration and time since quitting was observed, nonlinear relations were found. The risk of pancreatic cancer increased rapidly for each additional increment of one cigarette/day up to 25-30 cigarettes/day, and less rapidly for higher number of cigarettes/day. As regard duration, the risk increased rapidly for each year up to 25 years, then the risk increased less rapidly, plateaued at 35 years, and declined for subjects who smoked more than 40 years. Finally, a periodic effect was observed for years since quitting, although a decreased risk for each years was found.
Conclusions: This uniquely large pooled analysis confirms that current cigarette smoking is associated with a twofold increased risk of pancreatic cancer and that the risk, with a nonlinear effect, increases with the number of cigarettes smoked and duration of smoking and decreases with time since quitting.
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Souza, Erivaldo Lopes de. "Modelos aditivos generalizados para a avaliação da intenção de compra de consumidores." Universidade Federal da Paraíba, 2012. http://tede.biblioteca.ufpb.br:8080/handle/tede/5232.
Full textAbstract:
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Previous issue date: 2012-12-21Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
In recent years, several studies have been published dealing on factors that influence consumer purchase intent in various economic sectors. In this line of work, we are specifically for the sector of collective buying, obtain regression models that could contribute to the study of the relationship between the purchase intention and the characteristics of market segments. The aim is to assist in the inclusion of this variable purchase intention in the process of choosing a target group, guiding decisions to meet more effectively service consumers. To achieve this goal, initially interviewed 384 Internet users in the city of João Pessoa, Paraíba, Brazil. Then the data obtained from interviews, were used to estimate those models. These models were based on assumptions of theories of the cognitive approach of consumer behavior, especially the Theory of Reasoned Action. The instrument used for data collection was a questionnaire containing market research questions related to psychological factors, socio-cultural and situational consumer. The most successful model was a generalized additive model with nine variables and nonparametric one end, obtained from smoothing splines. This model had a pseudo-R2 of 0,89 and allowed to reach a percentage of correct trials of the observations of the sample equal to 94%. With the aid of simulations, it was observed how the proposed model type is capable of assisting in selection of a target with a higher interest in the use of the service. It was also shown how the model can be used to evaluate production systems, in relation to more efficient service to customers intend to use the service. The generalized additive models were effective for identifying the presence of nonlinear relationships and were able to generate a high explanatory power of the propensity of individuals to use specific service.
Nos últimos anos, vários estudos foram publicados versando sobre fatores que influenciam a intenção de compra do consumidor em diversos setores econômicos. Nesta linha de trabalho, procurou-se, especificamente para o setor de compra coletivas, obter modelos de regressão que pudessem contribuir para o estudo da relação entre a intenção de compra e as características de segmentos de mercado. Visa-se com isso auxiliar na inclusão da variável intenção de compras no processo de escolha de um público-alvo, orientando decisões para satisfazer com maior eficiência consumidores do serviço. Para alcançar o objetivo, entrevistaram-se inicialmente 384 usuários de Internet da cidade de João Pessoa, Paraíba, Brasil. Em seguida os dados obtidos a partir de entrevistas, foram usados para estimar aqueles modelos. Esses modelos foram baseados em pressupostos de teorias da abordagem cognitiva do comportamento do consumidor, especialmente da Teoria da Ação Racional. O instrumento usado para a coleta de dados foi um questionário de pesquisa de mercado contendo questões ligadas a fatores psicológicos, sócio-culturais e situacionais do consumidor. O modelo mais bem sucedido foi um modelo aditivo generalizado com nove variáveis e com um termo não paramétrico, obtido a partir do método de suavização splines. Esse modelo apresentou um pseudo-R2 igual a 0,89 e possibilitou alcançar um percentual de acertos nos julgamentos das observações da amostra igual a 94%. Com o auxílio de simulações, verificou-se de que modo o tipo de modelo proposto é capaz de auxiliar na escolha de um público-alvo com maior interesse no uso do serviço. Apresentou-se ainda a maneira pela qual o modelo pode ser usado para avaliar sistemas produtivos, em relação ao atendimento mais eficiente de clientes que têm a intenção de utilizar o serviço. Os modelos aditivos generalizados mostraram-se eficientes para identificar a presença de relações não lineares e foram capazes de gerar um poder explicativo alto da propensão de indivíduos para utilizar um serviço específico.
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Morellato, Saulo Almeida 1983. "Inferência estatística para regressão múltipla h-splines." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306505.
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Orientador: Ronaldo DiasTese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica
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Resumo: Este trabalho aborda dois problemas de inferência relacionados à regressão múltipla não paramétrica: a estimação em modelos aditivos usando um método não paramétrico e o teste de hipóteses para igualdade de curvas ajustadas a partir do modelo. Na etapa de estimação é construída uma generalização dos métodos h-splines, tanto no contexto sequencial adaptativo proposto por Dias (1999), quanto no contexto bayesiano proposto por Dias e Gamerman (2002). Os métodos h-splines fornecem uma escolha automática do número de bases utilizada na estimação do modelo. Estudos de simulação mostram que os resultados obtidos pelos métodos de estimação propostos são superiores aos conseguidos nos pacotes gamlss, mgcv e DPpackage em R. São criados dois testes de hipóteses para testar H0 : f = f0. Um teste de hipóteses que tem sua regra de decisão baseada na distância quadrática integrada entre duas curvas, referente à abordagem sequencial adaptativa, e outro baseado na medida de evidência bayesiana proposta por Pereira e Stern (1999). No teste de hipóteses bayesiano o desempenho da medida de evidência é observado em vários cenários de simulação. A medida proposta apresentou um comportamento que condiz com uma medida de evidência favorável à hipótese H0. No teste baseado na distância entre curvas, o poder do teste foi estimado em diversos cenários usando simulações e os resultados são satisfatórios. Os procedimentos propostos de estimação e teste de hipóteses são aplicados a um conjunto de dados referente ao trabalho de Tanaka e Nishii (2009) sobre o desmatamento no leste da Ásia. O objetivo é escolher um entre oito modelos candidatos. Os testes concordaram apontando um par de modelos como sendo os mais adequados
Abstract: In this work we discuss two inference problems related to multiple nonparametric regression: estimation in additive models using a nonparametric method and hypotheses testing for equality of curves, also considering additive models. In the estimation step, it is constructed a generalization of the h-splines method, both in the sequential adaptive context proposed by Dias (1999), and in the Bayesian context proposed by Dias and Gamerman (2002). The h-splines methods provide an automatic choice of the number of bases used in the estimation of the model. Simulation studies show that the results obtained by proposed estimation methods are superior to those achieved in the packages gamlss, mgcv and DPpackage in R. Two hypotheses testing are created to test H0 : f = f0. A hypotheses test that has a decision rule based on the integrated squared distance between two curves, for adaptive sequential approach, and another based on the Bayesian evidence measure proposed by Pereira and Stern (1999). In Bayesian hypothesis testing the performance measure of evidence is observed in several simulation scenarios. The proposed measure showed a behavior that is consistent with evidence favorable to H0. In the test based on the distance between the curves, the power of the test was estimated at various scenarios using simulations, and the results are satisfactory. At the end of the work the proposed procedures of estimation and hypotheses testing are applied in a dataset concerning to the work of Tanaka and Nishii (2009) about the deforestation in East Asia. The objective is to choose one amongst eight models. The tests point to a pair of models as being the most suitableIn this work we discuss two inference problems related to multiple nonparametric regression: estimation in additive models using a nonparametric method and hypotheses testing for equality of curves, also considering additive models. In the estimation step, it is constructed a generalization of the h-splines method, both in the sequential adaptive context proposed by Dias (1999), and in the Bayesian context proposed by Dias and Gamerman (2002). The h-splines methods provide an automatic choice of the number of bases used in the estimation of the model. Simulation studies show that the results obtained by proposed estimation methods are superior to those achieved in the packages gamlss, mgcv and DPpackage in R. Two hypotheses testing are created to test H0 : f = f0. A hypotheses test that has a decision rule based on the integrated squared distance between two curves, for adaptive sequential approach, and another based on the Bayesian evidence measure proposed by Pereira and Stern (1999). In Bayesian hypothesis testing the performance measure of evidence is observed in several simulation scenarios. The proposed measure showed a behavior that is consistent with evidence favorable to H0. In the test based on the distance between the curves, the power of the test was estimated at various scenarios using simulations, and the results are satisfactory. At the end of the work the proposed procedures of estimation and hypotheses testing are applied in a dataset concerning to the work of Tanaka and Nishii (2009) about the deforestation in East Asia. The objective is to choose one amongst eight models. The tests point to a pair of models as being the most suitable
Doutorado
Estatistica
Doutor em Estatística
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Monterubbianesi, María Gloria. "Evaluación de alternativas para el análisis estadístico y de aspectos del diseño en ensayos de larga duración para estudios agronómicos." Doctoral thesis, Universitat de Lleida, 2017. http://hdl.handle.net/10803/457696.
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Es van aplicar models lineals mixtes i models additius generalitzats per a localització, escala i forma per a modelar els canvis en el contingut del nitrogen mineral del sòl associats a l’ efecte del conreu, el temps i la interacció d’ambdós, en un assaig de llarga durada en agricultura (ELDA) a Catalunya, Espanya. Es van revisar aspectes de disseny per a quatre ELDA en el Sud-est Bonaerense, Argentina, amb sistemes de cultiu involucrant rotacions. En els casos en què es van detectar deficiències de disseny, es va indicar com solventar-les en el mateix assaig o en assajos futurs. També es van suggerir dissenys alternatius. Es van realitzar propostes d'anàlisis de la variable contingut de carboni orgànic del sòl en aquests ELDA. Totes les etapes de totes les anàlisis van ser implementades en l'ambient computacional R. Les sentències utilitzades, són la base per a la generalització de l'anàlisi estadística dels ELDA i la creació d'un paquet específic.Se aplicaron modelos lineales mixtos y modelos aditivos generalizados para localización, escala y forma para modelar los cambios en el contenido del nitrógeno mineral del suelo asociados al efecto de laboreo, tiempo y su interacción, en un ensayo de larga duración en agricultura (ELDA) en Catalunya, España. Se revisaron aspectos de diseño para cuatro ELDA en el Sudeste Bonaerense, Argentina, con sistemas de cultivos involucrando rotaciones de cultivos. En los casos en que se detectaron deficiencias de diseño, se indicó cómo salvarlas en el mismo ensayo o en ensayos futuros. También se sugirieron diseños alternativos. Se realizaron propuestas de análisis de la variable contenido de carbono orgánico del suelo en estos ELDA. Todas las etapas de todos los análisis fueron implementadas en el ambiente computacional R. Las sentencias utilizadas, son la base para la generalización del análisis estadístico de los ELDA y la creación de un paquete específico.
Mixed linear models and generalized additive models for localization, scale and shape were used to model changes of in soil mineral nitrogen content associated to the effect of tillage, time, and their interaction, in a long-term experiment on agriculture (ELDA) at Catalunya, Spain. Design aspects of four ELDA on cropping systems including crop rotations at the Southeastern Buenos Aires province, Argentina, were evaluated. Ways to overcome observed design deficiencies in the experiment itself or for new ELDA, were indicated. Some alternative designs, were also suggested. Innovative analysis mechanisms of the variable soil organic carbon content in these four ELDA, have been proposed. All the stages of all the analysis done and proposed were performed in the R computational environment. The computing sentences used are the base for the generalization of the statistical analysis of the ELDA and the creation of a specific package.
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Kuhnert, Petra Meta. "New methodology and comparisons for the analysis of binary data using Bayesian and tree based methods." Thesis, Queensland University of Technology, 2003.
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Tassinari, Wagner de Souza. "Modelagem espacial, temporal e longitudinal: diferentes abordagens do estudo da leptospirose urbana." reponame:Repositório Institucional da FIOCRUZ, 2009. https://www.arca.fiocruz.br/handle/icict/2539.
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Previous issue date: 2009(...) O objetivo desta tese foi modelar os fatores de risco associados à ocorrência de leptospirose urbana em diferentes contextos, com especial atenção para aspectos espaciais e temporais. Foram utilizadas técnicas de modelagem tais como, modelos generalizados aditivos e mistos. Também explorou-se técnicas de detecção de aglomerados espaço-temporais. (...)
Leptospirosis, a disease caused by pathogenic spirochete of the genus Leptospira, is one of the most widespread zoonoses in the world, considered a major public health problem associated with the lack of sanitation and poverty. It is endemic in Brazil, data from surveillance show that outbreaks of leptospirosis occur as cyclical annual epidemics during rainfalls. The aim of this thesis was modeling the risk factors associated with the occurrence of leptospirosis in di erent urban contexts, with particular attention to spatial and temporal aspects. We used some modeling techniques such as generalized additive and mixed models. Techniques for detection space-time clusters were also explored. This thesis has prioritized the use of free softwares - R, ubuntu linux operating system, LATEX , SatScan (this is not open source but free). This thesis was prepared in the form of three articles. In the rst article is presented a spatio-temporal analysis of leptospirosis cases occurrence in Rio de Janeiro between 1997 and 2002. Using the detection of space-time clusters - \outbreaks" method - were statistically signi cant only cluster ocorred in 1997 and 1998. Generalized Linear Mixed Models were used to evaluate the risk factors associated with the occurrence of cases that belonged to outbreaks in endemic cases. The cases belonging to the outbreaks are associated with the occurrence of rainfall over 4 mm (OR, 3.71; 95% CI, 1.83 - 7.51). There were no signi cant associations with socioeconomic covariates, in other words, being endemic or epidemic leptospirosis occurs in the same population. The second and third articles examined a seroprevalence survey and seroconversion cohort conducted in Pau da Lima community, Salvador, Bahia. In both Generalized Additive Models were used to t the exposure variables both in individuals and peridomicile context, as well as to estimate the spatial area of leptospirosis risk. The signi cant variables were: gender, age, presence of rats in the peridomicile, domicile near a trash collectin or an open sewer and domicile altitude above sea level. Studies show that individual and contextual variables explain much of the spatial variability of leptospirosis, but there are still factors that were not measured in the studies but which should be investigated. The maps of risk of seroprevalence and seroconversion show distinct regions where the spatial e ect is signi cantly di erent from the global average. It is still lack for a more robust integration between the professionals who develop and operate the GIS, epidemiologists and biostatistics. This integration represents an important advance enabling the development and use of these techniques in Public Health support. The study of prevalence and incidence of endemic areas, in the leptospirosis context, it is very complex and still grow up. The reunion of professional specialists from several areas of human knowledge (eg, clinicians, epidemiologists, geographers, biologists, statisticians, engineers, etc.), it is essential to advance the knowledge about the disease and their relationship to social inequality and environmental well to contribute to the creation of efficient and e ective measures to control endemic diseases.
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Asciutto, Emanuele. "Dinamiche spaziotemporali del merlano (Merlangius merlangus, Linnaeus 1758) nel Mar Adriatico centro-settentrionale." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23110/.
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Le caratteristiche fisiche e ambientali dello spazio influenzano la disposizione degli organismi. La distribuzione e la densità delle specie possono subire dei mutamenti spaziotemporali a causa di plurimi fattori, sia biotici che abiotici. Conoscere i tratti ecologici degli organismi e comprendere come essi reagiscano a questi fattori è fondamentale per fare predizioni sulle possibili condizioni future delle loro popolazioni. La specie oggetto di questo studio è il merlano (Merlangius merlangus) un pesce molto comune nel Mare del Nord, in Adriatico centro-settentrionale, nell’Egeo e nel Mar Nero, localmente anche importante sul mercato. Attraverso il programma di ricerca Mediterranean Trawl Survey (MEDITS), che prevede monitoraggi annuali di pesca a strascico scientifica, è stato possibile più informazioni su questa specie che, in Adriatico, risulta essere poco studiata. I dati ottenuti coprono gli anni che vanno dal 1994 al 2020. Essi sono stati analizzati al fine di trarre informazioni sulla struttura per classi di taglia, sugli effetti di variabili ambientali quali profondità e temperatura sulla densità, sulla distribuzione spaziale della densità e stime annuali della stessa. I risultati evidenziano come le taglie predominanti in ogni anno raramente raggiungano i 20cm corrispondenti al primo anno di età, ma si attestino in media intorno ai 10-15cm con una tendenza verso taglie maggiori a fine estate/inizio autunno. I modelli Generalized Additive Models (GAMs) impiegati nelle analisi statistiche mostrano come M. merlangus pare preferire temperature inferiori a 20°C e profondità fino a 100 metri. Negli anni non si sono evidenziate tendenze positive o negative della densità, ma picchi e valli più o meno alternati. L’Adriatico settentrionale è risultata l’area maggiormente caratterizzata dalla presenza di M. merlangus. La raccolta di informazioni di questo tipo consente di gettare le basi per indagini più specifiche di valutazione dello stato di questa risorsa.
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Molina, Julia Maria Borges. "Uso de área pelo boto-cinza, Sotalia guianensis, no estuário de Cananeia." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/21/21134/tde-17042018-134357/.
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A percepção e interpretação da interação de indivíduos e populações com o ambiente e a forma como tal relação condiciona sua distribuição espacial é questão-chave e recorrente em estudos ecológicos. Padrões de uso de área observados para populações emergem em ultima análise da variabilidade entre seus indivíduos em selecionar habitats e interagir com os mesmos. Este estudo teve como foco o uso de área pela população do boto-cinza, Sotalia guianensis, e sua variabilidade individual no estuário de Cananeia, localizado na costa sudeste do Brasil (25°03\' S; 47°55\' W), durante o verão e o inverno de 2015 e o verão de 2016. Parâmetros ambientais e geográficos (distâncias da desembocadura de rios, da entrada do estuário e de áreas urbanas, profundidade, maré e autocorrelação espacial) foram testados para explicar a distribuição da população e de seus indivíduos a partir de funções de probabilidade de seleção de recursos (RSPF) em modelos aditivos generalizados (GAM). Onze indivíduos fotoidentificados com 18 ou mais recapturas foram avaliados com o uso de modelos individuais de ocupação e sua interpretação foi subsidiada por estimativas de áreas domiciliares obtidas a partir de kerneis fixos de densidade. Nas três temporadas a população apresentou densidades de grupos desiguais ao longo do estuário e todas as variáveis, com exceção da distância de áreas urbanas, explicaram as probabilidades de presença observadas. Análises individuais revelaram discrepâncias nos tamanhos e disposição geográfica de áreas domiciliares e diferenças na composição e estimativa dos parâmetros selecionados para cada indivíduo. A variabilidade individual na população deve ter papel fundamental em termos de utilização do espaço e seleção de habitat pelo boto-cinza no estuário local.Understanding and interpreting the interaction of individuals and populations with the environment and how this relationship outlines their spatial distribution is a key question common in ecological studies. Area use patterns observed for populations are ultimately an outcome from individual variability in habitat selection and their interaction with such environments. Are use and habitat selection by the population of Guiana dolphins, Sotalia guianensis, and its individual variability were accessed in the Cananeia estuary (25°03\' S; 47°55\' W), southeastern Brazil, during the summer and winter of 2015 and the summer of 2016. Environmental and geographic parameters were estimated aiming to explain population distribution and differences within individuals. For this purpose, resource selection probability functions (RSPF) were applied in generalized additive models (GAM). Covariates tested included: distance to river mouths, distance to the estuary entrance, distance to urban areas, depth and tide. Geographic coordinates were used to model spatial autocorrelation. Eleven photo-identified individuals had their occupancy modelled and accessed in relation to their home range obtained from fixed kernel densities estimates. The population exhibited patchy group densities throughout the estuary in all seasons. Except from distance to urban areas all variables were selected in our final model for the population\'s RSPF. Individual analysis revealed discrepancies in size and location of home ranges which lead to remarkable differences in the composition and estimates of parameters selected in the models for each individual.
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Mendoza, Natalie Veronika Rondinel. "Estruturas unidimensionais e bidimensionais utilizando P-splines nos modelos mistos aditivos generalizados com aplicação na produção de cana-de-açúcar." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-22032018-145655/.
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Os P-splines de Eilers e Marx (1996) são métodos de suavização que é uma combinação de bases B-splines e uma penalização discreta sobre os coeficientes das bases utilizados para suavizar dados normais e não normais em uma ou mais dimensões, no caso de várias dimensões utiliza-se como suavização o produto tensor dos P-splines. Também os P-splines são utilizados como representação de modelos mistos Currie et al. (2006) pela presença de características tais como: efeitos fixos, efeitos aleatórios, correlação espacial ou temporal e utilizados em modelos mais generalizados tais como os modelos mistos lineares generalizados e modelos mistos aditivos generalizados. Neste trabalho apresentou-se toda a abordagem, metodologia e descrição dos P-splines como modelos mistos e como componentes das estruturas suavizadoras de variáveis unidimensionais e bidimensionais dos modelos mistos aditivos generalizados, mostrando essa abordagem e propondo seu uso em uma aplicação no comportamento dos níveis médios da produção de cana-de-açúcar sob a influência das alterações das variáveis climáticas como temperatura e precipitação, que foram medidos ao longo de 10 anos em cada mesorregião do Estado de São Paulo. O motivo de usar essa abordagem como método de suavização é que muitas vezes não é conhecido a tendência dessas covariáveis climáticas mas sabe-se que elas influenciam diretamente sobre a variável resposta. Além de permitir essa abordagem inclusão de efeitos fixos e aleatórios nos modelos a serem propostos, permitirá a inclusão do processo autoregressivo AR(1) como estrutura de correlação nos resíduos.P-splines of Eilers e Marx (1996) are methods of smoothing that is a combination of B-splines bases and penalty the coefficients of the bases used to smooth normal and non-normal data in one or more dimensions; in the case of several dimensions it is used as smoothing the tensor product of the P-splines. Also the P-splines are used as representation of mixed models Currie et al. (2006) by the presence of characteristics such as: fixed effects, random effects, spatial or temporal correlation and used in more generalized models such as generalized linear mixed models and generalized additive mixed models. In this work the whole approach, methodology and description of the P-splines as mixed models and as components of the smoothing structures of one-dimensional and two-dimensional variables of generalized additive mixed models were presented, showing this approach and proposing its application in the behavior of the average levels of sugarcane production, which is influenced by changes in climatic variables such as temperature and precipitation , which were measured over 10 years in each mesoregion of the state of São Paulo. The reason for using this approach as a smoothing method is that the tendency of these climate covariables is not know for the most part, but is known that they influence directly the response variable, besides allowing this approach to include fixed and random effects in the models to be proposed, will allow the inclusion of the autoregressive process AR(1) as a correlation structure in the residuos.
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Goosen, Johannes Christiaan. "Comparing generalized additive neural networks with multilayer perceptrons / Johannes Christiaan Goosen." Thesis, North-West University, 2011. http://hdl.handle.net/10394/5552.
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In this dissertation, generalized additive neural networks (GANNs) and multilayer perceptrons (MLPs) are studied
and compared as prediction techniques. MLPs are the most widely used type of artificial neural network
(ANN), but are considered black boxes with regard to interpretability. There is currently no simple a priori
method to determine the number of hidden neurons in each of the hidden layers of ANNs. Guidelines exist that
are either heuristic or based on simulations that are derived from limited experiments. A modified version of
the neural network construction with cross–validation samples (N2C2S) algorithm is therefore implemented and
utilized to construct good MLP models. This algorithm enables the comparison with GANN models. GANNs
are a relatively new type of ANN, based on the generalized additive model. The architecture of a GANN is less
complex compared to MLPs and results can be interpreted with a graphical method, called the partial residual
plot. A GANN consists of an input layer where each of the input nodes has its own MLP with one hidden layer.
Originally, GANNs were constructed by interpreting partial residual plots. This method is time consuming and
subjective, which may lead to the creation of suboptimal models. Consequently, an automated construction
algorithm for GANNs was created and implemented in the SAS R
statistical language. This system was called
AutoGANN and is used to create good GANN models.
A number of experiments are conducted on five publicly available data sets to gain insight into the similarities
and differences between GANN and MLP models. The data sets include regression and classification tasks.
In–sample model selection with the SBC model selection criterion and out–of–sample model selection with the
average validation error as model selection criterion are performed. The models created are compared in terms
of predictive accuracy, model complexity, comprehensibility, ease of construction and utility.
The results show that the choice of model is highly dependent on the problem, as no single model always
outperforms the other in terms of predictive accuracy. GANNs may be suggested for problems where interpretability
of the results is important. The time taken to construct good MLP models by the modified N2C2S
algorithm may be shorter than the time to build good GANN models by the automated construction algorithmThesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2011.
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Stein, Fabiano da Rocha. "Modelagem da produção industrial de celulose Kraft com modelos aditivos generalizados e redes neurais." Universidade Federal de Viçosa, 2010. http://locus.ufv.br/handle/123456789/5899.
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Previous issue date: 2010-07-08In this study, data collected on an industrial scale for some years, underwent modeling using generalized additive models (GAM) and artificial neural networks (ANN) as tools to evaluate the influence of some variables, timber and process on production and digester alkali charge. Generalized additive models were fitted using the software R (R Development Core Team, 2010), through the library "mgcv" (Wood, 2006), specific settings for generalized additive models. Significance tests were applied for each model set. In order to supplement the data were analyzed using artificial neural networks.One hundred RNA were adjusted to relate to production and the digester alkali charge, and variables such as wood density, age, precipitation, dry content of wood chips, bulk density of chips, the wood basic density, pulp viscosity and kappa. In this step we employed the software Statistica (Statsoft, Inc., 2007). The results show that the Generalized Additive Model (GAM) is a good choice to represent the phenomena of the pulp industry, where the variables are highly variable and there is strict control, unlike what happens on data from experimental designs. Should the use of RNA to estimate the output from the digester alkali charge and also proved a useful tool, since the correlations between actual and estimated data were above 88% and 60% respectively. Several variables associated with the raw material and the pulping process that were studied showed similar behavior and / or equal to what the majority of experimental studies have found.
No presente trabalho, dados observados em escala industrial, durante alguns anos, foram submetidos à modelagem empregando modelos aditivos generalizados (GAM) e redes neurais artificiais (RNA), como ferramentas para avaliar a influência de algumas variáveis, da madeira e do processo, sobre a produção do digestor e carga alcalina. Os modelos aditivos generalizados foram ajustados utilizando o software R (R DEVELOPMENT CORE TEAM, 2010), através da biblioteca “mgcv” (WOOD, 2006), específica para ajustes de modelos aditivos generalizados. Foram aplicados testes de significância para cada modelo ajustado. De forma complementar os dados foram analisados por meio de redes neurais artificiais. Foram ajustadas 100 RNA para relacionar a produção do digestor e a carga alcalina, com as variáveis: densidade da madeira, idade, precipitação, teor seco dos cavacos, densidade aparente dos cavacos, densidade básica dos cavacos, viscosidade da polpa e kappa. Nesta etapa do trabalho foi empregado o software Statistica (Statsoft, INC, 2007). Os resultados mostram que o Modelo Aditivo Generalizado (MAG) constitui uma boa opção para representar os fenômenos da indústria de celulose, em que as variáveis apresentam alta variabilidade e não há um rigoroso controle, diferentemente do que ocorre em dados provenientes de delineamentos experimentais. No caso do uso de RNA para estimar a produção do digestor e para carga alcalina também mostrou ser uma boa ferramenta, visto que as correlações entre os dados reais e estimados ficaram acima de 88% e 60%, respectivamente. Várias variáveis associadas com a matéria-prima e com o processo de polpação que foram estudadas apresentaram comportamento semelhante e/ou iguais o que a maioria dos estudos experimentais encontraram.
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Kaivanipour, Kivan. "Non-Life Insurance Pricing Using the Generalized Additive Model, Smoothing Splines and L-Curves." Thesis, KTH, Matematik (Avd.), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168389.
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In non-life insurance, almost every tariff analysis involves continuous rating variables, such as the age of the policyholder or the weight of the insured vehicle. In the generalized linear model, continuous rating variables are categorized into intervals and all values within an interval are treated as identical. By using the generalized additive model, the categorization part of the generalized linear model can be avoided. This thesis will treat different methods for finding the optimal smoothing parameter within the generalized additive model. While the method of cross validation is commonly used for this purpose, a more uncommon method, the L-curve method, is investigated for its performance in comparison to the method of cross validation. Numerical computations on test data show that the L-curve method is significantly faster than the method of cross validation, but suffers from heavy under-smoothing and is thus not a suitable method for estimating the optimal smoothing parameter.Nästan alla tariffanalyser inom sakförsäkring inkluderar kontinuerliga premieargument, såsom försäkringstagarens ålder eller vikten på det försäkrade fordonet. I den generaliserade linjära modellen så grupperas kontinuerliga premiearguments möjliga värden i intervaller och alla värden inom ett intervall behandlas som identiska. Genom att använda den generaliserade additativa modellen så slipper man arbetet med att dela in kontinuerliga premiearguments möjliga värden i intervaller. Detta examensarbete kommer att behandla olika metoder för att uppskatta den optimala smoothing-parametern inom den generaliserade additativa modellen. Metoden för korsvalidering används vanligen för detta ändamål. L-kurve-metoden, som är en mer ovanlig metod, undersöks för dess prestanda i jämförelse med metoden för korsvalidering. Numeriska beräkningar på testdata visar att L-kurve-metoden är betydligt snabbare än metoden för korsvalidering, men att den underutjämnar och därför inte är en lämplig metod för att uppskatta den optimala smoothing-parametern.
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Boruvka, Audrey. "Data-driven estimation for Aalen's additive risk model." Thesis, Kingston, Ont. : [s.n.], 2007. http://hdl.handle.net/1974/489.
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Campher, Susanna Elisabeth Sophia. "Comparing generalised additive neural networks with decision trees and alternating conditional expectations / Susanna E. S. Campher." Thesis, North-West University, 2008. http://hdl.handle.net/10394/2025.
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Du, Toit Jan Valentine. "Automated construction of generalized additive neural networks for predictive data mining / Jan Valentine du Toit." Thesis, North-West University, 2006. http://hdl.handle.net/10394/128.
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In this thesis Generalized Additive Neural Networks (GANNs) are studied in the context of predictive Data Mining. A GANN is a novel neural network implementation of a Generalized Additive Model. Originally GANNs were constructed interactively by considering partial residual plots.
This methodology involves subjective human judgment, is time consuming, and can result in suboptimal
results. The newly developed automated construction algorithm solves these difficulties by
performing model selection based on an objective model selection criterion. Partial residual plots
are only utilized after the best model is found to gain insight into the relationships between inputs
and the target. Models are organized in a search tree with a greedy search procedure that identifies
good models in a relatively short time. The automated construction algorithm, implemented
in the powerful SAS® language, is nontrivial, effective, and comparable to other model selection
methodologies found in the literature. This implementation, which is called AutoGANN, has a
simple, intuitive, and user-friendly interface. The AutoGANN system is further extended with an
approximation to Bayesian Model Averaging. This technique accounts for uncertainty about the
variables that must be included in the model and uncertainty about the model structure. Model
averaging utilizes in-sample model selection criteria and creates a combined model with better predictive
ability than using any single model. In the field of Credit Scoring, the standard theory of
scorecard building is not tampered with, but a pre-processing step is introduced to arrive at a more
accurate scorecard that discriminates better between good and bad applicants. The pre-processing
step exploits GANN models to achieve significant reductions in marginal and cumulative bad rates.
The time it takes to develop a scorecard may be reduced by utilizing the automated construction
algorithm.Thesis (Ph.D. (Computer Science))--North-West University, Potchefstroom Campus, 2006.
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Barbosa, Gerson Laurindo 1970. "Distribuição espacial dos indicadores entomológicos de Aedes aegypti e associação com a ocorrência de casos de dengue em município de médio porte do Estado de São Paulo." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/313023.
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Orientadores: Roberto Wagner Lourenço, Maria Rita Donalísio CordeiroTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Ciências Médicas
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Resumo: Diminuir os níveis de infestação pelo Aedes aegypti é uma das poucas estratégias para o controle da dengue na atualidade. O acompanhamento dos indicadores de infestação constitui parâmetro estratégico para as ações das equipes de controle da doença, porém pouco se sabe sobre a capacidade preditiva destes indicadores. Este trabalho tem como objetivo analisar a distribuição espacial dos indicadores entomológicos de Aedes aegypti nas fases ovos, larvas/pupas e mosquitos adultos e sua influência no risco de ocorrência de dengue em um município de médio porte no estado de São Paulo. Trata-se de um estudo caso-controle espacial, para avaliar a associação entre os indicadores entomológicos e o risco de dengue em Sumaré, SP, no ano de 2011. Os casos de dengue foram os confirmados e notificados pelo Sistema de Vigilância Epidemiológica da cidade e os controles foram obtidos por sorteio de pontos no perímetro da área habitada. Os indicadores entomológicos foram construídos a partir de coleta mensal de ovos (armadilhas), larvas/pupas e mosquitos adultos em quarteirões sorteados. Superfícies suavizadas dos valores dos indicadores entomológicos foram obtidas por meio do método de Krigagem ordinária. Estes indicadores foram incluídos no modelo aditivo generalizado para avaliar sua influência no risco espacial da doença. Observou-se ocorrência sazonal da doença e dos indicadores. Casos de dengue e vetores nas diversas fases do ciclo biológico foram encontrados em toda área de estudo. Entretanto, não houve coincidência espacial entre o risco da doença e a intensidade dos indicadores entomológicos. Os riscos relativos espaciais de dengue brutos e ajustados mostram feição espacial similar, indicando limitada interferência no risco da doença. Assim, a distribuição espacial e temporal da dengue possivelmente não depende da distribuição espacial dos vetores em locais onde os níveis de infestação são altos, antigos e estáveis, como no caso de Sumaré. Além disso, a área analisada apresenta infestação e transmissão antiga e deficiência de serviços públicos de saneamento e intensa circulação de pessoas, que podem ser fatores relevantes para explicar a circulação do vírus. O vetor foi identificado em abundância suficiente para desencadear e manter a circulação do vírus na área de estudo. A infestação não apresentou grande variação de intensidade e foi suficiente para a manutenção e/ou ocorrência de casos de dengue na área de estudo. O modelo aditivo generalizado não mostrou nenhum dos indicadores entomológicos analisados como preditores de áreas de risco de transmissão. A inclusão de outras variáveis nos modelos aditivos generalizados como sorotipos circulantes, imunidade populacional e intervenções por parte das equipes de controle poderiam eventualmente revelar efeito modulador do risco da doença, não encontrado utilizando-se apenas com os indicadores entomológicos
Abstract: Decrease the infestation levels of Aedes aegypti is one of the few strategies for dengue control today. Monitoring infestation indicators is strategic for the dengue control program, but little is known about the predictive capacity of these indicators. This study aimed to analyze the spatial distribution of entomological indicators of Aedes aegypti in the stages of egg, larva-pupae and adult forms and its influence on risk of dengue in a medium-sized city in the state of São Paulo. This is a spatial case-control study to evaluate the association between entomological indicators and risk of dengue in Sumaré, SP, in 2011. Dengue cases confirmed and reported by the Epidemiological Surveillance System of the municipality and the controls were obtained on the perimeter of the inhabited areas. Monthly entomological indicators were constructed from eggs, larvae-pupae and adult forms collected in the selected blocks. Smoothed surfaces for cases and entomological indicators were obtained by the ordinary kriging method. These indicators were included in the generalized additive model to assess its influence on the spatial risk of the disease. Seasonality of disease occurrence and entomological indicators were observed. Cases of dengue and vectors in the various life cycle stages were found throughout the study area. However, there was no spatial coincidence between disease risk and intensity of entomological indicators. The spatial crude and adjusted relative risks of dengue showed similar features, indicating its limited interference in disease risk. The spatial and temporal distribution of the disease may not depend exclusively on the spatial distribution of vectors in areas where infestation levels are high, longstanding and stable, like in the case of Sumaré-SP. Furthermore, the analyzed area has experienced dengue cases and high infestation for a long time and has poor public sanitation services and intense movement of persons, which may be relevant to explain the circulation of the virus. The vector was identified abundantly sufficient to initiate and maintain the virus in the study area. The infestation had no significant variation in intensity and was sufficient for the maintenance and / or occurrence of dengue cases in the study area. The entomological indicators analyzed in the generalized additive model didn¿t act as a predictor of the dengue risk in the area. Other variables as serotype circulation, the population immunity and interventions by the control teams could be included in the models in order to modulate disease risk, which was not found using only entomological indicators
Doutorado
Epidemiologia
Doutor em Saude Coletiva
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Yu, Hao. "Spatial and temporal population dynamics of yellow perch (Perca flavescens) in Lake Erie." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/28586.
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Yellow perch (Perca flavescens) in Lake Erie support valuable commercial and recreational fisheries critical to the local economy and society. The study of yellow perch's temporal and spatial population dynamics is important for both stock assessment and fisheries management. I explore the spatial and temporal variation of the yellow perch population by analyzing the fishery-independent surveys in Lake Erie. Model-based approaches were developed to estimate the relative abundance index, which reflected the temporal variation of the population. I also used design-based approaches to deal with the situation in which population density varied both spatially and temporally.
I first used model-based approaches to explore the spatial and temporal variation of the yellow perch population and to develop the relative abundance index needed. Generalized linear models (GLM), spatial generalized linear models (s-GLM), and generalized additive models (GAM) were compared by examining the goodness-of-fit, reduction of spatial autocorrelation, and prediction errors from cross-validation. The relationship between yellow perch density distribution and spatial and environmental factors was also studied. I found that GAM showed the best goodness-of-fit shown as AIC and lowest prediction errors but s-GLM resulted in the best reduction of spatial autocorrelation. Both performed better than GLM for yellow perch relative abundance index estimation. I then applied design-based approaches to study the spatial and temporal population dynamics of yellow perch through both practical data analysis and simulation. The currently used approach in Lake Erie is stratified random sampling (StRS). Traditional sampling designs (simple random sampling (SRS) and StRS) and adaptive sampling designs (adaptive two-phase sampling (ATS), adaptive cluster sampling (ACS), and adaptive two-stage sequential sampling (ATSS)) for fishery-independent surveys were compared. From accuracy and precision aspect, ATS performed better than the SRS, StRS, ACS and ATSS for yellow perch fishery-independent survey data in Lake Erie. Model-based approaches were further studied by including geostatistical models. The performance of the GLM and GAM models and geostatistical models (spatial interpolation) were compared when they are used to analyze the temporal and spatial variation of the yellow perch population through a simulation study. This is the first time that these two types of model- based approaches have been compared in fisheries. I found that arithmetic mean (AM) method was only preferred when neither environment factors nor spatial information of sampling locations were available. If the survey can not cover the distribution area of the population due to biased design or lack of sampling locations, GLMs and GAMs are preferable to spatial interpolation (SI). Otherwise, SI is a good alternative model to estimate relative abundance index. SI has rarely been realized in fisheries.
Different models may be recommended for different species/fisheries when we estimate their spatial-temporal dynamics, and also the most appropriate survey designs may be different for different species. However, the criteria and approaches for the comparison of both model-based and design-based approaches will be applied for different species or fisheries.Ph. D.
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Maurer, Dustin. "Comparison of background correction in tiling arrays and a spatial model." Kansas State University, 2011. http://hdl.handle.net/2097/12130.
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Master of ScienceDepartment of Statistics
Susan J. Brown
Haiyan Wang
DNA hybridization microarray technologies have made it possible to gain an unbiased perspective of whole genome transcriptional activity on such a scale that is increasing more and more rapidly by the day. However, due to biologically irrelevant bias introduced by the experimental process and the machinery involved, correction methods are needed to restore the data to its true biologically meaningful state. Therefore, it is important that the algorithms developed to remove any sort of technical biases are accurate and robust. This report explores the concept of background correction in microarrays by using a real data set of five replicates of whole genome tiling arrays hybridized with genetic material from Tribolium castaneum. It reviews the literature surrounding such correction techniques and explores some of the more traditional methods through implementation on the data set. Finally, it introduces an alternative approach, implements it, and compares it to the traditional approaches for the correction of such errors.
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Hayn, Michael, Steffen Beirle, Fred A. Hamprecht, Ulrich Platt, Björn H. Menze, and Thomas Wagner. "Analysing spatio-temporal patterns of the global NO2-distribution retrieved from GOME satellite observations using a generalized additive model." Universität Potsdam, 2009. http://opus.kobv.de/ubp/volltexte/2010/4499/.
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With the increasing availability of observational data from different sources at a global level, joint analysis of these data is becoming especially attractive. For such an analysis – oftentimes with little prior knowledge about local and global interactions between the different observational variables at hand – an exploratory, data-driven analysis of the data may be of particular relevance. In the present work we used generalized additive models (GAM) in an exemplary study of spatio-temporal patterns in the tropospheric NO2-distribution derived from GOME satellite observations (1996 to 2001) at global scale. We focused on identifying correlations between NO2 and local wind fields, a quantity which is of particular interest in the analysis of spatio-temporal interactions. Formulating general functional, parametric relationships between the observed NO2 distribution and local wind fields, however, is difficult – if not impossible. So, rather than following a modelbased analysis testing the data for predefined hypotheses (assuming, for example, sinusoidal seasonal trends), we used a GAM with non-parametric model terms to learn this functional relationship between NO2 and wind directly from the data. The NO2 observations showed to be affected by winddominated processes over large areas. We estimated the extent of areas affected by specific NO2 emission sources, and were able to highlight likely atmospheric transport “pathways”. General temporal trends which were also part of our model – weekly, seasonal and linear changes – showed to be in good agreement with previous studies and alternative ways of analysing the time series. Overall, using a non-parametric model provided favorable means for a rapid inspection of this large spatio-temporal NO2 data set, with less bias than parametric approaches, and allowing to visualize dynamical processes of the NO2 distribution at a global scale.
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Aarts, Geert. "Modelling space-use and habitat preference from wildlife telemetry data." Thesis, St Andrews, 2007. http://hdl.handle.net/10023/327.
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Liu, Wenjie. "Estimation and bias correction of the magnitude of an abrupt level shift." Thesis, Linköpings universitet, Statistik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-84618.
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Consider a time series model which is stationary apart from a single shift in mean. If the time of a level shift is known, the least squares estimator of the magnitude of this level shift is a minimum variance unbiased estimator. If the time is unknown, however, this estimator is biased. Here, we first carry out extensive simulation studies to determine the relationship between the bias and three parameters of our time series model: the true magnitude of the level shift, the true time point and the autocorrelation of adjacent observations. Thereafter, we use two generalized additive models to generalize the simulation results. Finally, we examine to what extent the bias can be reduced by multiplying the least squares estimator with a shrinkage factor. Our results showed that the bias of the estimated magnitude of the level shift can be reduced when the level shift does not occur close to the beginning or end of the time series. However, it was not possible to simultaneously reduce the bias for all possible time points and magnitudes of the level shift.