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Nuthmann, Antje, Wolfgang Einhäuser, and Immo Schütz. "How Well Can Saliency Models Predict Fixation Selection in Scenes Beyond Central Bias? A New Approach to Model Evaluation Using Generalized Linear Mixed Models." Universitätsbibliothek Chemnitz, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-232614.
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Since the turn of the millennium, a large number of computational models of visual salience have been put forward. How best to evaluate a given model's ability to predict where human observers fixate in images of real-world scenes remains an open research question. Assessing the role of spatial biases is a challenging issue; this is particularly true when we consider the tendency for high-salience items to appear in the image center, combined with a tendency to look straight ahead (“central bias”). This problem is further exacerbated in the context of model comparisons, because some—but not all—models implicitly or explicitly incorporate a center preference to improve performance. To address this and other issues, we propose to combine a-priori parcellation of scenes with generalized linear mixed models (GLMM), building upon previous work. With this method, we can explicitly model the central bias of fixation by including a central-bias predictor in the GLMM. A second predictor captures how well the saliency model predicts human fixations, above and beyond the central bias. By-subject and by-item random effects account for individual differences and differences across scene items, respectively. Moreover, we can directly assess whether a given saliency model performs significantly better than others. In this article, we describe the data processing steps required by our analysis approach. In addition, we demonstrate the GLMM analyses by evaluating the performance of different saliency models on a new eye-tracking corpus. To facilitate the application of our method, we make the open-source Python toolbox “GridFix” available.
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Carvalho, Rafael Augusto Pincante de. "Fatores determinantes da intensidade de uso dos abrigos pela geneta (Genetta genetta L. 1758) numa região mediterrânica." Master's thesis, Universidade de Évora, 2012. http://hdl.handle.net/10174/15506.
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A disponibilidade de locais de abrigo é um elemento chave para a persistência e conservação de
populações de carnívoros. Este trabalho avaliou a influência de gradientes ecológicos na seleção e
intensidade de uso de abrigos pela geneta. Foram caracterizados os abrigos de 21 genetas sujeitas a radioseguimento
diário, entre Maio de 2010 e Janeiro de 2012. Usaram-se modelos lineares mistos para modelar
4 gradientes ecológicos definidos com base numa Análise de Componentes Principais a partir das variáveis
explicativas originais.
A tranquilidade (31%), a heterogeneidade da paisagem (23%), o relevo (17%) e a insolação (10%)
explicaram 81% da variância na proporção de uso dos abrigos. As genetas usam mais abrigos em árvores,
localizados em áreas tranquilas, expostos a sul, com um relevo moderadamente acidentado em zonas
homogéneas de montado ou ninhos construídos na vegetação em áreas mais diversas do ponto de vista
paisagístico, perto de ribeiras com galeria ripícola.
A incorporação desta informação, na gestão de áreas florestais, possibilitará manter a qualidade e
diversidade dos abrigos, condição necessária à viabilidade futura das populações dos pequenos carnívoros
florestais; ABSTRACT:The availability of resting sites is a key element for the conservation and persistence of wild
carnivore populations. Our study aimed to describe how the ecological gradients influence the selection
and intensity of use of resting sites by the genet. We characterized the resting sites of 21 common genets
followed by radio telemetry between May 2010 and January 2012, on a daily basis. Generalized linear
mixed models were used to model 4 ecological gradients obtained from a Principal Component Analysis
based on the original explanatory variables .
Tranquillity (31%), landscape heterogeneity (23%), landscape roughness (17%) and insolation (10
%) explained 81% of the variance in the resting site selection and intensity use. Genets use mainly tree
hollows, located on quiet places, south orientated, in moderately hilly areas of homogeneous montado or
they use vegetation nests in more heterogeneous landscapes located near watercourses with riparian gallery.
The incorporation of this information on forest management plans will maintain the quality and
diversity of resting sites, which are key conditions for the future viability of small forest carnivores.
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Gory, Jeffrey J. "Marginally Interpretable Generalized Linear Mixed Models." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1497966698387606.
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Tang, On-yee, and 鄧安怡. "Estimation for generalized linear mixed model via multipleimputations." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B30687652.
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Sepato, Sandra Moepeng. "Generalized linear mixed model and generalized estimating equation for binary longitudinal data." Diss., University of Pretoria, 2014. http://hdl.handle.net/2263/43143.
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The most common analysis used for binary data is generalised linear model (GLM) with either
a binomial or bernoulli distribution using either a logit, probit, complementary log-log
or other type of link functions. However, such analyses violate the independence assumption
if the binary data are measured repeatedly over time at the same subject or site. Failure to
take into account the correlation can lead to incorrect estimation of regression parameters
and the estimates are less efficient, particularly when the correlations are large. Therefore,
to obtain the most efficient estimates that are also unbiased the methods that incorporate
correlations (McCullagh and Nelder, 1989) should be used. Two of the statistical methodologies
that can be used to account for this correlation for the longitudinal data are the
generalized linear mixed models (GLMMs) and generalized estimating equation (GEE).
The GLMM method is based on extending the fixed effects GLM to include random effects
and covariance patterns. Unlike the GLM and GLMM methods, the GEE method is based
on the quasi-likelihood theory and no assumption is made about the distribution of response
observations (Liang and Zeger, 1986). The main objective of the study is to investigate the
statistical properties and limitations of these three approaches, i.e. GLM, GLMMs and GEE
for analyzing longitudinal data through use of a binary data from an entomology study. The
results reaffirms the point made by these authors that misspecification of working correlation
in GEE approach would still give consistent regression parameter estimates. Further, the
results of this study suggest that even with small correlation, ignoring a random effects in
a binary model can lead to inconsistent estimation.Dissertation (MSc)--University of Pretoria, 2014.
lk2014
Statistics
MSc
Unrestricted
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Tang, On-yee. "Estimation for generalized linear mixed model via multiple imputations." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B30687652.
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Chen, Jinsong. "Semiparametric Methods for the Generalized Linear Model." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/28012.
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The generalized linear model (GLM) is a popular model in many research areas. In the GLM, each outcome of the dependent variable is assumed to be generated from a particular distribution function in the exponential family. The mean of the distribution depends on the independent variables. The link function provides the relationship between the linear predictor and the mean of the distribution function. In this dissertation, two semiparametric extensions of the GLM will be developed. In the first part of this dissertation, we have proposed a new model, called a semiparametric generalized linear model with a log-concave random component (SGLM-L). In this model, the estimate of the distribution of the random component has a nonparametric form while the estimate of the systematic part has a parametric form. In the second part of this dissertation, we have proposed a model, called a generalized semiparametric single-index mixed model (GSSIMM). A nonparametric component with a single index is incorporated into the mean function in the generalized linear mixed model (GLMM) assuming that the random component is following a parametric distribution.
In the first part of this dissertation, since most of the literature on the GLM deals with the parametric random component, we relax the parametric distribution assumption for the random component of the GLM and impose a log-concave constraint on the distribution. An iterative numerical algorithm for computing the estimators in the SGLM-L is developed. We construct a log-likelihood ratio test for inference. In the second part of this dissertation, we use a single index model to generalize the GLMM to have a linear combination of covariates enter the model via a nonparametric mean function, because the linear model in the GLMM is not complex enough to capture the underlying relationship between the response and its associated covariates. The marginal likelihood is approximated using the Laplace method. A penalized quasi-likelihood approach is proposed to estimate the nonparametric function and parameters including single-index coe±cients in the GSSIMM. We estimate variance components using marginal quasi-likelihood. Asymptotic properties of the estimators are developed using a similar idea by Yu (2008). A simulation example is carried out to compare the performance of the GSSIMM with that of the GLMM. We demonstrate the advantage of my approach using a study of the association between daily air pollutants and daily mortality adjusted for temperature and wind speed in various counties of North Carolina.Ph. D.
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Yam, Ho-kwan, and 任浩君. "On a topic of generalized linear mixed models and stochastic volatility model." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B29913342.
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Sima, Adam. "Accounting for Model Uncertainty in Linear Mixed-Effects Models." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/2950.
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Standard statistical decision-making tools, such as inference, confidence intervals and forecasting, are contingent on the assumption that the statistical model used in the analysis is the true model. In linear mixed-effect models, ignoring model uncertainty results in an underestimation of the residual variance, contributing to hypothesis tests that demonstrate larger than nominal Type-I errors and confidence intervals with smaller than nominal coverage probabilities. A novel utilization of the generalized degrees of freedom developed by Zhang et al. (2012) is used to adjust the estimate of the residual variance for model uncertainty. Additionally, the general global linear approximation is extended to linear mixed-effect models to adjust the standard errors of the parameter estimates for model uncertainty. Both of these methods use a perturbation method for estimation, where random noise is added to the response variable and, conditional on the observed responses, the corresponding estimate is calculated. A simulation study demonstrates that when the proposed methodologies are utilized, both the variance and standard errors are inflated for model uncertainty. However, when a data-driven strategy is employed, the proposed methodologies show limited usefulness. These methods are evaluated with a trial assessing the performance of cervical traction in the treatment of cervical radiculopathy.
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Zhan, Tingting. "The Generalized Linear Mixed Model for Finite Normal Mixtures with Application to Tendon Fibrilogenesis Data." Diss., Temple University Libraries, 2012. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/171613.
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StatisticsPh.D.
We propose the generalized linear mixed model for finite normal mixtures (GLMFM), as well as the estimation procedures for the GLMFM model, which are widely applicable to the hierarchical dataset with small number of individual units and multi-modal distributions at the lowest level of clustering. The modeling task is two-fold: (a). to model the lowest level cluster as a finite mixtures of the normal distribution; and (b). to model the properly transformed mixture proportions, means and standard deviations of the lowest-level cluster as a linear hierarchical structure. We propose the robust generalized weighted likelihood estimators and the new cubic-inverse weight for the estimation of the finite mixture model (Zhan et al., 2011). We propose two robust methods for estimating the GLMFM model, which accommodate the contaminations on all clustering levels, the standard-two-stage approach (Chervoneva et al., 2011, co-authored) and a robust joint estimation. Our research was motivated by the data obtained from the tendon fibril experiment reported in Zhang et al. (2006). Our statistical methodology is quite general and has potential application in a variety of relatively complex statistical modeling situations.
Temple University--Theses
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Chen, Yin. "Quasi-Monte Carlo methods in generalized linear mixed model with correlated and non-normal random effects." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.516829.
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Codd, Casey. "A Review and Comparison of Models and Estimation Methods for Multivariate Longitudinal Data of Mixed Scale Type." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398686513.
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Cho, Jang Ik. "Partial EM Procedure for Big-Data Linear Mixed Effects Model, and Generalized PPE for High-Dimensional Data in Julia." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case152845439167999.
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Wang, Yu. "A study on the type I error rate and power for generalized linear mixed model containing one random effect." Kansas State University, 2017. http://hdl.handle.net/2097/35301.
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Master of ScienceDepartment of Statistics
Christopher Vahl
In animal health research, it is quite common for a clinical trial to be designed to demonstrate the efficacy of a new drug where a binary response variable is measured on an individual experimental animal (i.e., the observational unit). However, the investigational treatments are applied to groups of animals instead of an individual animal. This means the experimental unit is the group of animals and the response variable could be modeled with the binomial distribution. Also, the responses of animals within the same experimental unit may then be statistically dependent on each other. The usual logit model for a binary response assumes that all observations are independent. In this report, a logit model with a random error term representing the group of animals is considered. This is model belongs to a class of models referred to as generalized linear mixed models and is commonly fit using the SAS System procedure PROC GLIMMIX. Furthermore, practitioners often adjust the denominator degrees of freedom of the test statistic produced by PROC GLIMMIX using one of several different methods. In this report, a simulation study was performed over a variety of different parameter settings to compare the effects on the type I error rate and power of two methods for adjusting the denominator degrees of freedom, namely “DDFM = KENWARDROGER” and “DDFM = NONE”. Despite its reputation for fine performance in linear mixed models with normally distributed errors, the “DDFM = KENWARDROGER” option tended to perform poorly more often than the “DDFM = NONE” option in the logistic regression model with one random effect.
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Barbosa, Luciano [UNESP]. "Metodologias estatísticas na análise de germinação de sementes de mamona." Universidade Estadual Paulista (UNESP), 2010. http://hdl.handle.net/11449/101848.
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barbosa_l_dr_botfca.pdf: 2587351 bytes, checksum: 76e343f1e0edbbbee5cb996188d8efd2 (MD5)É bastante comum na área agrícola, experimentos cujas variáveis respostas são contagens ou proporções. Para esse tipo de dados, utiliza-se a metodologia de modelos lineares generalizados quando as respostas são independentes. Por outro lado, quando as respostas são dependentes, há uma correlação entre as observações e isso tem que ser levado em consideração na análise, para evitar inferências incorretas sobre os coeficientes de regressão. Na literatura há técnicas disponíveis para a modelagem e análise desses dados, sendo os modelos disponíveis extensões dos modelos lineares generalizados. No presente trabalho, utiliza-se a metodologia de equação de estimação generalizada, que inclui no modelo uma matriz de correlação para a obtenção de um melhor ajuste. Outra alternativa, também abordada neste trabalho, é a utilização de um modelo linear generalizado misto, no qual o uso de efeitos aleatórios também introduz uma correlação entre observações que tenham algum efeito em comum. Essas duas metodologias são aplicadas a um conjunto de dados obtidos de um experimento para avaliar certas condições na germinação de sementes de mamona da cultivar AL Guarany 2002, com o objetivo de se verificar qual o melhor modelo de estimação para esses dados
Experiments whose response variables are counts or proportions are very common in agriculture. For this type of data, if the observational units are independent, the methodology of generalized linear models can be appropriate. On the other hand, when responses are dependent or clustered, there is a correlation between the observations and that has to be taken into consideration in the analysis to avoid incorrect inferences about the regression coefficients. In the literature there are techniques available for modeling and analyzing such type data, the models being extensions of generalized linear models. The present study explores the use of: 1) generalized estimation equations, that includes a correlation matrix to obtain a better fit; 2) generalized linear mixed models, that introduce a correlation between clustered observations though the addition of random effects in the model. These two methodologies are applied to a data set obtained from an experiment to evaluate certain conditions on the germination of seeds of castor bean cultivar AL Guarany 2002 with the objective of determining the best estimation model for such data
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Barbosa, Luciano 1971. "Metodologias estatísticas na análise de germinação de sementes de mamona /." Botucatu : [s.n.], 2010. http://hdl.handle.net/11449/101848.
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Orientador: Luiza Aparecida TrincaBanca: Liciana Vaz da Arruda
Banca: Osmar Delmanto Junior
Banca: Célia Regina Lopes Zimback
Banca: Marli Teixeira de A. Minhoni
Resumo: É bastante comum na área agrícola, experimentos cujas variáveis respostas são contagens ou proporções. Para esse tipo de dados, utiliza-se a metodologia de modelos lineares generalizados quando as respostas são independentes. Por outro lado, quando as respostas são dependentes, há uma correlação entre as observações e isso tem que ser levado em consideração na análise, para evitar inferências incorretas sobre os coeficientes de regressão. Na literatura há técnicas disponíveis para a modelagem e análise desses dados, sendo os modelos disponíveis extensões dos modelos lineares generalizados. No presente trabalho, utiliza-se a metodologia de equação de estimação generalizada, que inclui no modelo uma matriz de correlação para a obtenção de um melhor ajuste. Outra alternativa, também abordada neste trabalho, é a utilização de um modelo linear generalizado misto, no qual o uso de efeitos aleatórios também introduz uma correlação entre observações que tenham algum efeito em comum. Essas duas metodologias são aplicadas a um conjunto de dados obtidos de um experimento para avaliar certas condições na germinação de sementes de mamona da cultivar AL Guarany 2002, com o objetivo de se verificar qual o melhor modelo de estimação para esses dados
Abstract: Experiments whose response variables are counts or proportions are very common in agriculture. For this type of data, if the observational units are independent, the methodology of generalized linear models can be appropriate. On the other hand, when responses are dependent or clustered, there is a correlation between the observations and that has to be taken into consideration in the analysis to avoid incorrect inferences about the regression coefficients. In the literature there are techniques available for modeling and analyzing such type data, the models being extensions of generalized linear models. The present study explores the use of: 1) generalized estimation equations, that includes a correlation matrix to obtain a better fit; 2) generalized linear mixed models, that introduce a correlation between clustered observations though the addition of random effects in the model. These two methodologies are applied to a data set obtained from an experiment to evaluate certain conditions on the germination of seeds of castor bean cultivar AL Guarany 2002 with the objective of determining the best estimation model for such data
Doutor
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SALISBURY, SHEILIA. "The Multivariate Generalized Linear Mixed Model for a Joint Modeling Approach for Analysis of Tumor Multiplicity Data: Development and Comparison of Methods." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1202404654.
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Chao, Yi. "Bayesian Hierarchical Latent Model for Gene Set Analysis." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/32060.
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Pathway is a set of genes which are predefined and serve a particular celluar or physiological function. Ranking pathways relevant to a particular phenotype can help researchers focus on a few sets of genes in pathways. In this thesis, a Bayesian hierarchical latent model was proposed using generalized linear random effects model. The
advantage of the approach was that it can easily incorporate prior knowledges when the sample size was small and the number of genes was large. For the covariance matrix of a set of random variables, two Gaussian random processes were considered to construct the dependencies among genes in a pathway. One was based on the polynomial kernel and the other was based on the Gaussian kernel. Then these two kernels were compared with constant covariance matrix of the random effect by using the ratio, which was based on the joint posterior distribution with respect to each model. For
mixture models, log-likelihood values were computed at different values of the mixture proportion, compared among mixtures of selected kernels and point-mass density (or constant covariance matrix). The approach was applied to a data set (Mootha et al., 2003) containing the expression profiles of type II diabetes where the motivation was to identify pathways that can discriminate between normal patients and patients with type II diabetes.Master of Science
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Mahmoud, Hamdy Fayez Farahat. "Some Advanced Semiparametric Single-index Modeling for Spatially-Temporally Correlated Data." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/76744.
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Semiparametric modeling is a hybrid of the parametric and nonparametric modelings where some function forms are known and others are unknown. In this dissertation, we have made several contributions to semiparametric modeling based on the single index model related to the following three topics: the first is to propose a model for detecting change points simultaneously with estimating the unknown function; the second is to develop two models for spatially correlated data; and the third is to further develop two models for spatially-temporally correlated data.
To address the first topic, we propose a unified approach in its ability to simultaneously estimate the nonlinear relationship and change points. We propose a single index change point model as our unified approach by adjusting for several other covariates. We nonparametrically estimate the unknown function using kernel smoothing and also provide a permutation based testing procedure to detect multiple change points. We show the asymptotic properties of the permutation testing based procedure. The advantage of our approach is demonstrated using the mortality data of Seoul, Korea from January, 2000 to December, 2007.
On the second topic, we propose two semiparametric single index models for spatially correlated data. One additively separates the nonparametric function and spatially correlated random effects, while the other does not separate the nonparametric function and spatially correlated random effects. We estimate these two models using two algorithms based on Markov Chain Expectation Maximization algorithm. Our approaches are compared using simulations, suggesting that the semiparametric single index nonadditive model provides more accurate estimates of spatial correlation. The advantage of our approach is demonstrated using the mortality data of six cities, Korea from January, 2000 to December, 2007.
The third topic involves proposing two semiparametric single index models for spatially and temporally correlated data. Our first model has the nonparametric function which can separate from spatially and temporally correlated random effects. We refer it to "semiparametric spatio-temporal separable single index model (SSTS-SIM)", while the second model does not separate the nonparametric function from spatially correlated random effects but separates the time random effects. We refer our second model to "semiparametric nonseparable single index model (SSTN-SIM)". Two algorithms based on Markov Chain Expectation Maximization algorithm are introduced to simultaneously estimate parameters, spatial effects, and times effects. The proposed models are then applied to the mortality data of six major cities in Korea. Our results suggest that SSTN-SIM is more flexible than SSTS-SIM because it can estimate various nonparametric functions while SSTS-SIM enforces the similar nonparametric curves. SSTN-SIM also provides better estimation and prediction.Ph. D.
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Barbu, Otilia C. "Using PROC GLIMMIX to Analyze the Animal Watch, a Web-Based Tutoring System for Algebra Readiness." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/238636.
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In this study, I investigated how proficiently seventh-grade students enrolled in two Southwestern schools solve algebra word problems. I analyzed various factors that could affect this proficiency and explored the differences between English Learners (ELs) and native English Primary students (EPs). I collected the data as part of the Animal Watch project, a computer-based initiative designed to improve the mathematical skills of children from grades 5-8 in the Southwest. A sample of 86 students (26 ELs and 60 EPs), clustered in four different classes, was used for this project. A Generalized Linear Mixed Model (GLMM) approach with the GLIMMIX procedure in SAS 9.3 showed that students from the classes that had a higher percentage of EL students performed better than those in the classes where the EL concentration was lower. Classes with more EL males were better at learning mathematics than classes with more EP females. The results also indicated: (a) a positive correlation between the students' ability to solve algebra word problems on their first attempt and their success ratio in solving all problems, and (b) a negative correlation between the percentage of problems solved correctly and those considered too hard from the very beginning. I conclude my dissertation by making specific recommendations for further research.
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Shen, Xia. "Novel Statistical Methods in Quantitative Genetics : Modeling Genetic Variance for Quantitative Trait Loci Mapping and Genomic Evaluation." Doctoral thesis, Uppsala universitet, Beräknings- och systembiologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-170091.
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This thesis develops and evaluates statistical methods for different types of genetic analyses, including quantitative trait loci (QTL) analysis, genome-wide association study (GWAS), and genomic evaluation. The main contribution of the thesis is to provide novel insights in modeling genetic variance, especially via random effects models. In variance component QTL analysis, a full likelihood model accounting for uncertainty in the identity-by-descent (IBD) matrix was developed. It was found to be able to correctly adjust the bias in genetic variance component estimation and gain power in QTL mapping in terms of precision. Double hierarchical generalized linear models, and a non-iterative simplified version, were implemented and applied to fit data of an entire genome. These whole genome models were shown to have good performance in both QTL mapping and genomic prediction. A re-analysis of a publicly available GWAS data set identified significant loci in Arabidopsis that control phenotypic variance instead of mean, which validated the idea of variance-controlling genes. The works in the thesis are accompanied by R packages available online, including a general statistical tool for fitting random effects models (hglm), an efficient generalized ridge regression for high-dimensional data (bigRR), a double-layer mixed model for genomic data analysis (iQTL), a stochastic IBD matrix calculator (MCIBD), a computational interface for QTL mapping (qtl.outbred), and a GWAS analysis tool for mapping variance-controlling loci (vGWAS).
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Apanasovich, Tatiyana Vladimirovna. "Testing for spatial correlation and semiparametric spatial modeling of binary outcomes with application to aberrant crypt foci in colon carcinogenesis experiments." Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/2674.
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In an experiment to understand colon carcinogenesis, all animals were exposed to a carcinogen while half the animals were also exposed to radiation. Spatially, we measured the existence of aberrant crypt foci (ACF), namely morphologically changed colonic crypts that are known to be precursors of colon cancer development. The biological question of interest is whether the locations of these ACFs are spatially correlated: if so, this indicates that damage to the colon due to carcinogens and radiation is localized. Statistically, the data take the form of binary outcomes (corresponding to the existence of an ACF) on a regular grid. We develop score??type methods based upon the Matern and conditionally autoregression (CAR) correlation models to test for the spatial correlation in such data, while allowing for nonstationarity. Because of a technical peculiarity of the score??type test, we also develop robust versions of the method. The methods are compared to a generalization of Moran??s test for continuous outcomes, and are shown via simulation to have the potential for increased power. When applied to our data, the methods indicate the existence of spatial correlation, and hence indicate localization of damage. Assuming that there are correlations in the locations of the ACF, the questions are how great are these correlations, and whether the correlation structures di?er when an animal is exposed to radiation. To understand the extent of the correlation, we cast the problem as a spatial binary regression, where binary responses arise from an underlying Gaussian latent process. We model these marginal probabilities of ACF semiparametrically, using ?xed-knot penalized regression splines and single-index models. We ?t the models using pairwise pseudolikelihood methods. Assuming that the underlying latent process is strongly mixing, known to be the case for many Gaussian processes, we prove asymptotic normality of the methods. The penalized regression splines have penalty parameters that must converge to zero asymptotically: we derive rates for these parameters that do and do not lead to an asymptotic bias, and we derive the optimal rate of convergence for them. Finally, we apply the methods to the data from our experiment.
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Chen, Chen. "Evaluating Time-varying Effect in Single-type and Multi-type Semi-parametric Recurrent Event Models." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/64371.
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This dissertation aims to develop statistical methodologies for estimating the effects of time-fixed and time-varying factors in recurrent events modeling context. The research is motivated by the traffic safety research question of evaluating the influence of crash on driving risk and driver behavior. The methodologies developed, however, are general and can be applied to other fields. Four alternative approaches based on various data settings are elaborated and applied to 100-Car Naturalistic Driving Study in the following Chapters.
Chapter 1 provides a general introduction and background of each method, with a sketch of 100-Car Naturalistic Driving Study. In Chapter 2, I assessed the impact of crash on driving behavior by comparing the frequency of distraction events in per-defined windows. A count-based approach based on mixed-effect binomial regression models was used.
In Chapter 3, I introduced intensity-based recurrent event models by treating number of Safety Critical Incidents and Near Crash over time as a counting process. Recurrent event models fit the natural generation scheme of the data in this study. Four semi-parametric models are explored: Andersen-Gill model, Andersen-Gill model with stratified baseline functions, frailty model, and frailty model with stratified baseline functions. I derived model estimation procedure and and conducted model comparison via simulation and application.
The recurrent event models in Chapter 3 are all based on proportional assumption, where effects are constant. However, the change of effects over time is often of primary interest. In Chapter 4, I developed time-varying coefficient model using penalized B-spline function to approximate varying coefficients. Shared frailty terms was used to incorporate correlation within subjects. Inference and statistical test are also provided. Frailty representation was proposed to link time-varying coefficient model with regular frailty model.
In Chapter 5, I further extended framework to accommodate multi-type recurrent events with time-varying coefficient. Two types of recurrent-event models were developed. These models incorporate correlation among intensity functions from different type of events by correlated frailty terms. Chapter 6 gives a general review on the contributions of this dissertation and discussion of future research directions.Ph. D.
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Oliveira, Izabela Regina Cardoso de. "Modeling strategies for complex hierarchical and overdispersed data in the life sciences." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-12082014-105135/.
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In this work, we study the so-called combined models, generalized linear mixed models with extension to allow for overdispersion, in the context of genetics and breeding. Such flexible models accommodates cluster-induced correlation and overdispersion through two separate sets of random effects and contain as special cases the generalized linear mixed models (GLMM) on the one hand, and commonly known overdispersion models on the other. We use such models while obtaining heritability coefficients for non-Gaussian characters. Heritability is one of the many important concepts that are often quantified upon fitting a model to hierarchical data. It is often of importance in plant and animal breeding. Knowledge of this attribute is useful to quantify the magnitude of improvement in the population. For data where linear models can be used, this attribute is conveniently defined as a ratio of variance components. Matters are less simple for non-Gaussian outcomes. The focus is on time-to-event and count traits, where the Weibull-Gamma-Normal and Poisson-Gamma-Normal models are used. The resulting expressions are sufficiently simple and appealing, in particular in special cases, to be of practical value. The proposed methodologies are illustrated using data from animal and plant breeding. Furthermore, attention is given to the occurrence of negative estimates of variance components in the Poisson-Gamma-Normal model. The occurrence of negative variance components in linear mixed models (LMM) has received a certain amount of attention in the literature whereas almost no work has been done for GLMM. This phenomenon can be confusing at first sight because, by definition, variances themselves are non-negative quantities. However, this is a well understood phenomenon in the context of linear mixed modeling, where one will have to make a choice between a hierarchical and a marginal view. The variance components of the combined model for count outcomes are studied theoretically and the plant breeding study used as illustration underscores that this phenomenon can be common in applied research. We also call attention to the performance of different estimation methods, because not all available methods are capable of extending the parameter space of the variance components. Then, when there is a need for inference on such components and they are expected to be negative, the accuracy of the method is not the only characteristic to be considered.Neste trabalho foram estudados os chamados modelos combinados, modelos lineares generalizados mistos com extensão para acomodar superdispersão, no contexto de genética e melhoramento. Esses modelos flexíveis acomodam correlação induzida por agrupamento e superdispersão por meio de dois conjuntos separados de efeitos aleatórios e contem como casos especiais os modelos lineares generalizados mistos (MLGM) e os modelos de superdispersão comumente conhecidos. Tais modelos são usados na obtenção do coeficiente de herdabilidade para caracteres não Gaussianos. Herdabilidade é um dos vários importantes conceitos que são frequentemente quantificados com o ajuste de um modelo a dados hierárquicos. Ela é usualmente importante no melhoramento vegetal e animal. Conhecer esse atributo é útil para quantificar a magnitude do ganho na população. Para dados em que modelos lineares podem ser usados, esse atributo é convenientemente definido como uma razão de componentes de variância. Os problemas são menos simples para respostas não Gaussianas. O foco aqui é em características do tipo tempo-até-evento e contagem, em que os modelosWeibull-Gama-Normal e Poisson-Gama-Normal são usados. As expressões resultantes são suficientemente simples e atrativas, em particular nos casos especiais, pelo valor prático. As metodologias propostas são ilustradas usando dados de melhoramento animal e vegetal. Além disso, a atenção é voltada à ocorrência de estimativas negativas de componentes de variância no modelo Poisson-Gama- Normal. A ocorrência de componentes de variância negativos em modelos lineares mistos (MLM) tem recebido certa atenção na literatura enquanto quase nenhum trabalho tem sido feito para MLGM. Esse fenômeno pode ser confuso a princípio porque, por definição, variâncias são quantidades não-negativas. Entretanto, este é um fenômeno bem compreendido no contexto de modelagem linear mista, em que a escolha deverá ser feita entre uma interpretação hierárquica ou marginal. Os componentes de variância do modelo combinado para respostas de contagem são estudados teoricamente e o estudo de melhoramento vegetal usado como ilustração confirma que esse fenômeno pode ser comum em pesquisas aplicadas. A atenção também é voltada ao desempenho de diferentes métodos de estimação, porque nem todos aqueles disponíveis são capazes de estender o espaço paramétrico dos componentes de variância. Então, quando há a necessidade de inferência de tais componentes e é esperado que eles sejam negativos, a acurácia do método de estimação não é a única característica a ser considerada.
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Zahid, Saman. "Comparing Resource Abundance And Intake At The Reda And Wisla River Estuaries." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-172770.
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The migratory birds stop at different stopover sites during migration. The presence of resources in these stopover sites is essential to regain the energy of these birds. This thesis aims to compare the resource abundance and intake at the two stopover sites: Reda and Wisla river estuaries. How a bird's mass changes during its stay at an estuary is considered as a proxy for the resource abundance of a site. The comparison is made on different subsets, including those which has incomplete data, i.e. next day is not exactly one day after the previous capture. Multiple linear regression, Generalized additive model and Linear mixed effect model are used for analysis. Expectation maximization and an iterative predictive process are implemented to deal with incomplete data. We found that Reda has higher resource abundance and intake as compared to that of Wisla river estuary.
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Villavicencio, Gastelu Joel [UNESP]. "Análise espacial do potencial fotovoltaico em telhados de residências usando modelagem hierárquica bayesiana." Universidade Estadual Paulista (UNESP), 2016. http://hdl.handle.net/11449/137801.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
No presente trabalho tem-se como objetivo estimar o potencial fotovoltaico devido à instalação de sistemas fotovoltaicos em telhados de áreas residenciais. Na estimação desse potencial foram consideradas quatro grandezas: o nível de irradiação solar, a área aproveitável de telhado para a instalação dos sistemas fotovoltaicos, a eficiência de conversão dos sistemas fotovoltaicos e as probabilidades de instalação dos sistemas fotovoltaicos, que caracterizam as preferências dos habitantes à instalação desses sistemas. Um modelo hierárquico bayesiano foi proposto para o cálculo das probabilidades de instalação dos sistemas fotovoltaicos. Nesse modelo bayesiano é estabelecida uma relação entre as probabilidades de instalação, as variáveis socioeconômicas e as interações entre as subáreas, através de um modelo linear generalizado misto. O cálculo do valor esperado das probabilidades de instalação foi realizado usando o método de Monte Carlo via cadeias de Markov. Os resultados do potencial fotovoltaico são apresentados através de mapas temáticos, que permitem a visualização da distribuição espacial do seu valor esperado. Esta informação pode ajudar as concessionárias de distribuição no planejamento e expansão de suas redes elétricas em regiões com maior potencial de geração fotovoltaica.
The present work aims to estimate the photovoltaic potential for installing solar panel on the rooftop of residential areas. The estimation of this potential considers four quantities: the solar radiation level, rooftop availability for installation of photovoltaic systems, conversion efficiency of the photovoltaic systems and the probabilities for the installation of photovoltaic systems that characterize the preferences of the inhabitants to the installation of such systems. A bayesian hierarchical model is proposed to calculate the installation probabilities of photovoltaic systems. This bayesian model establishes a relation among the installation probabilities, socioeconomic variables and interactions between subareas, through a generalized linear mixed model. The calculation of expected value of installation probabilities in each subarea is performed using the Markov Chain Monte Carlo method. Photovoltaic potential results are presented through thematic maps that allow the visualization of the spatial distribution of its expected value. This information can help to distribution utilities for planning and expansion of their networks in regions with the greatest potential for photovoltaic generation.
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Villavicencio, Gastelu Joel. "Análise espacial do potencial fotovoltaico em telhados de residências usando modelagem hierárquica bayesiana /." Ilha Solteira, 2016. http://hdl.handle.net/11449/137801.
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Orientador: Antônio Padilha FeltrinResumo: No presente trabalho tem-se como objetivo estimar o potencial fotovoltaico devido à instalação de sistemas fotovoltaicos em telhados de áreas residenciais. Na estimação desse potencial foram consideradas quatro grandezas: o nível de irradiação solar, a área aproveitável de telhado para a instalação dos sistemas fotovoltaicos, a eficiência de conversão dos sistemas fotovoltaicos e as probabilidades de instalação dos sistemas fotovoltaicos, que caracterizam as preferências dos habitantes à instalação desses sistemas. Um modelo hierárquico bayesiano foi proposto para o cálculo das probabilidades de instalação dos sistemas fotovoltaicos. Nesse modelo bayesiano é estabelecida uma relação entre as probabilidades de instalação, as variáveis socioeconômicas e as interações entre as subáreas, através de um modelo linear generalizado misto. O cálculo do valor esperado das probabilidades de instalação foi realizado usando o método de Monte Carlo via cadeias de Markov. Os resultados do potencial fotovoltaico são apresentados através de mapas temáticos, que permitem a visualização da distribuição espacial do seu valor esperado. Esta informação pode ajudar as concessionárias de distribuição no planejamento e expansão de suas redes elétricas em regiões com maior potencial de geração fotovoltaica.
Abstract: The present work aims to estimate the photovoltaic potential for installing solar panel on the rooftop of residential areas. The estimation of this potential considers four quantities: the solar radiation level, rooftop availability for installation of photovoltaic systems, conversion efficiency of the photovoltaic systems and the probabilities for the installation of photovoltaic systems that characterize the preferences of the inhabitants to the installation of such systems. A bayesian hierarchical model is proposed to calculate the installation probabilities of photovoltaic systems. This bayesian model establishes a relation among the installation probabilities, socioeconomic variables and interactions between subareas, through a generalized linear mixed model. The calculation of expected value of installation probabilities in each subarea is performed using the Markov Chain Monte Carlo method. Photovoltaic potential results are presented through thematic maps that allow the visualization of the spatial distribution of its expected value. This information can help to distribution utilities for planning and expansion of their networks in regions with the greatest potential for photovoltaic generation.
Mestre
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Abel, Leah A. "Development and maintenance of victimization associated with bullying during the transition to middle school: The role of school-based factors." Kent State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=kent1594745288709797.
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Sagara, Issaka. "Méthodes d'analyse statistique pour données répétées dans les essais cliniques : intérêts et applications au paludisme." Thesis, Aix-Marseille, 2014. http://www.theses.fr/2014AIXM5081/document.
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De nombreuses études cliniques ou interventions de lutte ont été faites ou sont en cours en Afrique pour la lutte contre le fléau du paludisme. En zone d'endémie, le paludisme est une maladie récurrente. La revue de littérature indique une application limitée des outils statistiques appropriés existants pour l'analyse des données récurrentes de paludisme. Nous avons mis en oeuvre des méthodes statistiques appropriées pour l'analyse des données répétées d'essais thérapeutiques de paludisme. Nous avons également étudié les mesures répétées d'hémoglobine lors du suivi de traitements antipaludiques en vue d'évaluer la tolérance ou sécurité des médicaments en regroupant les données de 13 essais cliniques.Pour l'analyse du nombre d'épisodes de paludisme, la régression binomiale négative a été mise en oeuvre. Pour modéliser la récurrence des épisodes de paludisme, quatre modèles ont été utilisés : i) Les équations d'estimation généralisées (GEE) utilisant la distribution de Poisson; et trois modèles qui sont une extension du modèle Cox: ii) le modèle de processus de comptage d'Andersen-Gill (AG-CP), iii) le modèle de processus de comptage de Prentice-Williams-Peterson (PWP-CP); et iv) le modèle de Fragilité partagée de distribution gamma. Pour l'analyse de sécurité, c'est-à-dire l'évaluation de l'impact de traitements antipaludiques sur le taux d'hémoglobine ou la survenue de l'anémie, les modèles linéaires et latents généralisés mixtes (« GLLAMM : generalized linear and latent mixed models ») ont été mis en oeuvre. Les perspectives sont l'élaboration de guides de bonnes pratiques de préparation et d'analyse ainsi que la création d'un entrepôt des données de paludismeNumerous clinical studies or control interventions were done or are ongoing in Africa for malaria control. For an efficient control of this disease, the strategies should be closer to the reality of the field and the data should be analyzed appropriately. In endemic areas, malaria is a recurrent disease. Repeated malaria episodes are common in African. However, the literature review indicates a limited application of appropriate statistical tools for the analysis of recurrent malaria data. We implemented appropriate statistical methods for the analysis of these data We have also studied the repeated measurements of hemoglobin during malaria treatments follow-up in order to assess the safety of the study drugs by pooling data from 13 clinical trials.For the analysis of the number of malaria episodes, the negative binomial regression has been implemented. To model the recurrence of malaria episodes, four models were used: i) the generalized estimating equations (GEE) using the Poisson distribution; and three models that are an extension of the Cox model: ii) Andersen-Gill counting process (AG-CP), iii) Prentice-Williams-Peterson counting process (PWP-CP); and (iv) the shared gamma frailty model. For the safety analysis, i.e. the assessment of the impact of malaria treatment on hemoglobin levels or the onset of anemia, the generalized linear and latent mixed models (GLLAMM) has been implemented. We have shown how to properly apply the existing statistical tools in the analysis of these data. The prospects of this work remain in the development of guides on good practices on the methodology of the preparation and analysis and storage network for malaria data
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Mukherjee, Soumyadeep. "Antenatal Stressful Life Events and Postpartum Depression in the United States: the Role of Women’s Socioeconomic Status at the State Level." FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/2631.
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The purpose of this dissertation was to examine patterns of antenatal stressful life events (SLEs) experienced by women in the United States (U.S.) and their association with postpartum depression (PPD). It further explored the role of women's state-level socio-economic status (SES) on PPD; the racial/ethnic dispartites in SLE-PPD relationship; and the role of provider communication on perinatal depression.
Data from 2009–11 Pregnancy Risk Assessment Monitoring System (PRAMS) and SES indicators published by the Institute of Women’s Policy Research (IWPR) were used. Latent class analysis (LCA) was performed to identify unobserved class membership based on antenatal SLEs. Multilevel generalized linear mixed models examined whether state-level SES moderated the antenatal SLE-PPD relationship. Of 116,595 respondents to the PRAMS 2009-11, the sample size for our analyses ranged from 78% to 99%.
The majority (64%) of participants were in low-stress class. The illness/death related-stress class (13%) had a high prevalence of severe illness (77%) and death (63%) of a family member or someone very close to them, while those in the multiple-stress (22%) class endorsed most other SLEs. Eleven percent had PPD; women who experienced all types of stressors, had the highest odds (adjusted odds ratio [aOR]: 5.43; 95% confidence interval [CI]: 5.36, 5.51) of PPD. The odds of PPD decreased with increasing state-level social/economic autonomy index (aOR: 0.75; 95% CI: 0.64, 0.88), with significant cross-level interaction between stressors and state-level SES. Among non-Hispanic blacks and non-Hispanic whites, husband/partner not wanting the pregnancy (aOR: 1.47; 95% CI: 1.14, 1.90) and drug/drinking problems of someone close (aOR: 1.37; 95% CI: 1.21, 1.55) were respectively associated with PPD. Provider communication was protective.
That 1 out of every 5 and 1 out of every 8 women were in the high- and emotional-stress classes suggests that SLEs are common among pregnant women. Our results suggest that screening for antenatal SLEs might help identify women at risk for PPD. The finding that the odds of PPD decrease with increasing social/economic autonomy, could have policy implications and motivate efforts to improve these indices. This study also indicates the benefits of antenatal health care provider communication on perinatal depression.
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Hecht, Martin. "Optimierung von Messinstrumenten im Large-scale Assessment." Doctoral thesis, Humboldt-Universität zu Berlin, Lebenswissenschaftliche Fakultät, 2015. http://dx.doi.org/10.18452/17270.
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Messinstrumente stellen in der wissenschaftlichen Forschung ein wesentliches Element zur Erkenntnisgewinnung dar. Das Besondere an Messinstrumenten im Large-scale Assessment in der Bildungsforschung ist, dass diese normalerweise für jede Studie neu konstruiert werden und dass die Testteilnehmer verschiedene Versionen des Tests bekommen. Hierbei ergeben sich potentielle Gefahren für die Akkuratheit und Validität der Messung. Um solche Gefahren zu minimieren, sollten (a) die Ursachen für Verzerrungen der Messung und (b) mögliche Strategien zur Optimierung der Messinstrumente eruiert werden. Deshalb wird in der vorliegenden Dissertation spezifischen Fragestellungen im Rahmen dieser beiden Forschungsanliegen nachgegangen.Measurement instruments are essential elements in the acquisition of knowledge in scientific research. Special features of measurement instruments in large-scale assessments of student achievement are their frequent reconstruction and the usage of different test versions. Here, threats for the accuracy and validity of the measurement may emerge. To minimize such threats, (a) sources for potential bias of measurement and (b) strategies to optimize measuring instruments should be explored. Therefore, the present dissertation investigates several specific topics within these two research areas.
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Karimi, Maryam. "Modélisation conjointe de trajectoire socioprofessionnelle individuelle et de la survie globale ou spécifique." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLS120/document.
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Appartenir à une catégorie socio-économique moins élevée est généralement associé à une mortalité plus élevée pour de nombreuses causes de décès. De précédentes études ont déjà montré l’importance de la prise en compte des différentes dimensions des trajectoires socio-économiques au cours de la vie. L’analyse des trajectoires professionnelles constitue une étape importante pour mieux comprendre ces phénomènes. L’enjeu pour mesurer l’association entre les parcours de vie des trajectoires socio-économiques et la mortalité est de décomposer la part respective de ces facteurs dans l’explication du niveau de survie des individus. La complexité de l’interprétation de cette association réside dans la causalité bidirectionnelle qui la sous-tend: Les différentiels de mortalité sont-ils dus à des différentielsd’état de santé initial influençant conjointement la situation professionnelle et la mortalité, ou l’évolution professionnelle influence-t-elle directement l’état de santé puis la mortalité?Les méthodes usuelles ne tiennent pas compte de l’interdépendance des changements de situation professionnelle et de la bidirectionnalité de la causalité qui conduit à un biais important dans l’estimation du lien causale entre situation professionnelle et mortalité. Par conséquent, il est nécessaire de proposer des méthodes statistiques qui prennent en compte des mesures répétées (les professions) simultanément avec les variables de survie. Cette étude est motivée par la base de données Cosmop-DADS qui est un échantillon de la population salariée française.Le premier objectif de cette thèse était d’examiner l’ensemble des trajectoires professionnelles avec une classification professionnelle précise, au lieu d’utiliser un nombre limité d’états dans un parcours professionnel qui a été considéré précédemment. A cet effet, nous avons défini des variables dépendantes du temps afinde prendre en compte différentes dimensions des trajectoires professionnelles, à travers des modèles dits de "life-course", à savoir critical period, accumulation model et social mobility model, et nous avons mis en évidence l’association entre les trajectoires professionnelles et la mortalité par cause en utilisant ces variables dans un modèle de Cox.Le deuxième objectif a consisté à intégrer les épisodes professionnel comme un sous-modèle longitudinal dans le cadre des modèles conjoints pour réduire le biais issude l’inclusion des covariables dépendantes du temps endogènes dans le modèle de Cox. Nous avons proposé un modèle conjoint pour les données longitudinales nominaleset des données de risques concurrents dans une approche basée sur la vraisemblance. En outre, nous avons proposé une approche de type méta-analyse pour résoudre les problèmes liés au temps des calculs dans les modèles conjoints appliqués à l’analyse des grandes bases de données. Cette approche consiste à combiner les résultats issus d’analyses effectuées sur les échantillons stratifiés indépendants. Dans la même perspective de l’utilisation du modèle conjoint sur les grandes bases de données, nous avons proposé une procédure basée sur l’avantage computationnel de la régression de Poisson.Cette approche consiste à trouver les trajectoires typesà travers les méthodes de la classification, et d’appliquerle modèle conjoint sur ces trajectoires typesBeing in low socioeconomic position is associated with increased mortality risk from various causes of death. Previous studies have already shown the importance of considering different dimensions of socioeconomic trajectories across the life-course. Analyses of professional trajectories constitute a crucial step in order to better understand the association between socio-economic position and mortality. The main challenge in measuring this association is then to decompose the respectiveshare of these factors in explaining the survival level of individuals. The complexity lies in the bidirectional causality underlying the observed associations:Are mortality differentials due to differences in the initial health conditions that are jointly influencing employment status and mortality, or the professional trajectory influences directly health conditions and then mortality?Standard methods do not consider the interdependence of changes in occupational status and the bidirectional causal effect underlying the observed association and that leads to substantial bias in estimating the causal link between professional trajectory and mortality. Therefore, it is necessary to propose statistical methods that consider simultaneously repeated measurements (careers) and survivalvariables. This study was motivated by the Cosmop-DADS database, which is a sample of the French salaried population.The first aim of this dissertation was to consider the whole professional trajectories and an accurate occupational classification, instead of using limitednumber of stages during life course and a simple occupational classification that has been considered previously. For this purpose, we defined time-dependent variables to capture different life course dimensions, namely critical period, accumulation model and social mobility model, and we highlighted the association between professional trajectories and cause-specific mortality using the definedvariables in a Cox proportional hazards model.The second aim was to incorporate the employment episodes in a longitudinal sub-model within the joint model framework to reduce the bias resulting from the inclusion of internal time-dependent covariates in the Cox model. We proposed a joint model for longitudinal nominal outcomes and competing risks data in a likelihood-based approach. In addition, we proposed an approach mimicking meta-analysis to address the calculation problems in joint models and large datasets, by extracting independent stratified samples from the large dataset, applying the joint model on each sample and then combining the results. In the same objective, that is fitting joint model on large-scale data, we propose a procedure based on the appeal of the Poisson regression model. This approach consist of finding representativetrajectories by means of clustering methods and then applying the joint model on these representative trajectories
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Aljafary, Michelle. "Accounting for potential nonlinearity between catch and effort using meta-analysis and applying GLM and GLMM to fishing data from deployments of fixed and mobile gear." 2016. http://hdl.handle.net/1993/31208.
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My thesis examines nonlinearity between catch and effort. I use a meta-analysis of published literature and generalized linear mixed-effects models (GLMM) on both fixed and mobile gear fisheries of Atlantic Canada. The meta-analysis examines the proportionality of catch to effort using the slope of the reduced major axis (RMA) log-log regression, which accounts for “errors-in-variables”. The GLMMs explored proportionality while accounting for variation among fishing vessels. Both analyses found evidence for disproportionality between catch and effort. Catch that increases disproportionally to effort could result from either facilitation or recruitment of effort into the fishery. Catch increases that are less than proportional are expected from competitive interactions among fishers or gear saturation. The GLMM also revealed that the level of aggregation (by set, trip, monthly, or annually) can affect the apparent proportionality between catch and effort. In general, catch and effort should not be considered to be proportional.May 2016
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Gagnon, Jacob A. "A hierarchical spherical radial quadrature algorithm for multilevel GLMMs, GSMMs, and gene pathway analysis." 2010. https://scholarworks.umass.edu/dissertations/AAI3427529.
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The first part of my thesis is concerned with estimation for longitudinal data using generalized semi-parametric mixed models and multilevel generalized linear mixed models for a binary response. Likelihood based inferences are hindered by the lack of a closed form representation. Consequently, various integration approaches have been proposed. We propose a spherical radial integration based approach that takes advantage of the hierarchical structure of the data, which we call the 2 SR method. Compared to Pinheiro and Chao’s multilevel Adaptive Gaussian quadrature [37], our proposed method has an improved time complexity with the number of functional evaluations scaling linearly in the number of subjects and in the dimension of random effects per level. Simulation studies show that our approach has similar to better accuracy compared to Gauss Hermite Quadrature (GHQ) and has better accuracy compared to PQL especially in the variance components. The second part of my thesis is concerned with identifying differentially expressed gene pathways/gene sets. We propose a logistic kernel machine to model the gene pathway effect with a binary response. Kernel machines were chosen since they account for gene interactions and clinical covariates. Furthermore, we established a connection between our logistic kernel machine with GLMMs allowing us to use ideas from the GLMM literature. For estimation and testing, we adopted Clarkson’s spherical radial approach [6] to perform the high dimensional integrations. For estimation, our performance in simulation studies is comparable to better than Bayesian approaches at a much lower computational cost. As for testing of the genetic pathway effect, our REML likelihood ratio test has increased power compared to a score test for simulated non-linear pathways. Additionally, our approach has three main advantages over previous methodologies: (1) our testing approach is self-contained rather than competitive, (2) our kernel machine approach can model complex pathway effects and gene-gene interactions, and (3) we test for the pathway effect adjusting for clinical covariates. Motivation for our work is the analysis of an Acute Lymphocytic Leukemia data set where we test for the genetic pathway effect and provide confidence intervals for the fixed effects.
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Chen, Hsiang-Chun. "Inference for Clustered Mixed Outcomes from a Multivariate Generalized Linear Mixed Model." Thesis, 2013. http://hdl.handle.net/1969.1/151145.
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Multivariate generalized linear mixed models (MGLMM) are used for jointly modeling the clustered mixed outcomes obtained when there are two or more responses repeatedly measured on each individual in scientific studies. The relationship among these responses is often of interest. In the clustered mixed data, the correlation could be present between repeated measurements either within the same observer or between different observers on the same subjects. This study proposes a series of in- dices, namely, intra, inter and total correlation coefficients, to measure the correlation under various circumstances of observations from a multivariate generalized linear model, especially for joint modeling of clustered count and continuous outcomes.
Bayesian methods are widely used techniques for analyzing MGLMM. The need for noninformative priors arises when there is insufficient prior information on the model parameters. Another aim of this study is to propose an approximate uniform shrinkage prior for the random effect variance components in the Bayesian analysis for the MGLMM. This prior is an extension of the approximate uniform shrinkage prior. This prior is easy to apply and is shown to possess several nice properties. The methods are illustrated in terms of both a simulation study and a case example.
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Binici, Salih. "Random-effect differential item functioning via hierarchical generalized linear model and generalized linear latent mixed model a comparison of estimation methods /." 2007. http://etd.lib.fsu.edu/theses/available/etd-05082007-190825.
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Thesis (Ph. D.)--Florida State University, 2007.Advisor: Akihito Kamata, Florida State University, College of Education, Dept. of Educational Psychology and Learning Systems. Title and description from dissertation home page (viewed Sept. 19, 2007). Document formatted into pages; contains xiii, 175 pages. Includes bibliographical references.
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CHIANG, HSIEN-YU, and 蔣賢煜. "Association Analysis for Juvenile Misbehavior :Application of Generalized Linear Mixed Model." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/av353m.
Full textAbstract:
碩士東吳大學
數學系
102
The focus of this study is to explore factors contributing to juvenile’s misbehavior. Based on the literature, the analysis was performed by considering seven variables from the categories of family, school, friends, and urbanization. These variables are significant in the univariate analysis. The use of student samples is known to produce estimation errors caused by the cluster effect. Lin (2008) and Clark(2007) have shown using “school” as a fixed effect in the model can solve problems of estimation error. In contrast, Clarke (2010) demonstrated that employing “school” as a random effect in a model produces a better estimated result. The purposes of this thesis are as follow: First, use multivariate analysis to explore the connection between juvenile’s misbehavior and the seven chosen variables. Secondly, compare the result of assigning "school type" as a fixed effect and random effect into the model. The finding of this research indicated all factors were significant in both models. The outcome of parameter estimation was better when “school type” was added as a random effect in the model. Conversely, the chance for students to become misbehaving increases when they experience the following situations: less companionship from family, hanging out with misbehaving peers, low rate of urbanization and attending school that has a higher population of misbehaving students. Furthermore, students who often have conflicts with their father and attend school with negative school culture leads to higher chance of having misbehaving friends.
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Elmasri, Mohamad. "A Skew-Normal Copula-Driven Generalized Linear Mixed Model for Longitudinal Data." Thesis, 2012. http://spectrum.library.concordia.ca/973992/1/Elmasri_MSc_S2012.pdf.
Full textAbstract:
Using the advancements of Arellano-Valle et al. [2005], which characterize the likelihood function of a linear mixed model (LMM) under a skew-normal distribution for the random effects, this thesis attempt to construct a copula-driven generalized linear mixed model (GLMM). Assuming a multivariate distribution from the exponential family for the response variable and a skew-normal copula, we drive a complete characterization of the general likelihood function. For estimation, we apply a Monte Carlo expectation maximization (MC-EM) algorithm. Some special cases are discussed, in particular, the exponential and gamma distributions. Simulations with multiple link functions are shown alongside a real data example from the Framingham Heart
Study.
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Tsai, Fang-Yu, and 蔡芳榆. "Estimating Control Rates of Three Different Insecticides by Generalized Estimating Equation and Generalized Linear Mixed Model." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/05460098030021084419.
Full textAbstract:
碩士國立臺灣大學
農藝學研究所
93
The aim of this study is to estimate the control rate of three bait-formulated insecticides of red imported fire ants. Two field experiments were conducted, respectively, in Taoyuan and Chiayi county where the red ant infestation were spotted and the three different insecticides applied are Fipronil, Pyripronxyfen and Spinosyns. Repeated counts of ant mound number in each field plot of size 100 $m^2$ were recorded by the researchers in the local agricultural experimental station during the period of eight weeks. Two statistical procedures were employed to analyzed these two data sets and both are of generalized linear models. First one is a GEE model and the second one is a generalized mixed-effects model (GLMM). The former is relatively easy however the later demands more effort to determinea decent model. The estimates of control rate resulted from GEE and GLMM are quite similar though the standard errors are different substantially. We recommend that the SE''s due to GLMM be applied to construct relevant confidence intervals, since variance structure of GLMM does have a better description to the variation of data collected. One interesting result is that all three insecticides show remarkable consistancy in control rates in the two experiment sites.
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Chen, Nai-Wei. "Goodness-of-Fit Test Issues in Generalized Linear Mixed Models." Thesis, 2011. http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10504.
Full textAbstract:
Linear mixed models and generalized linear mixed models are random-effects models widely applied to analyze clustered or hierarchical data. Generally, random effects are often assumed to be normally distributed in the context of mixed models. However, in the mixed-effects logistic model, the violation of the assumption of normally distributed random effects may result in inconsistency for estimates of some fixed effects and the variance component of random effects when the variance of the random-effects distribution is large. On the other hand, summary statistics used for assessing goodness of fit in the ordinary logistic regression models may not be directly applicable to the mixed-effects logistic models. In this dissertation, we present our investigations of two independent studies related to goodness-of-fit tests in generalized linear mixed models.
First, we consider a semi-nonparametric density representation for the random effects distribution and provide a formal statistical test for testing normality of the random-effects distribution in the mixed-effects logistic models. We obtain estimates of parameters by using a non-likelihood-based estimation procedure. Additionally, we not only evaluate the type I error rate of the proposed test statistic through asymptotic results, but also carry out a bootstrap hypothesis testing procedure to control the inflation of the type I error rate and to study the power performance of the proposed test statistic. Further, the methodology is illustrated by revisiting a case study in mental health.
Second, to improve assessment of the model fit in the mixed-effects logistic models, we apply the nonparametric local polynomial smoothed residuals over within-cluster continuous covariates to the unweighted sum of squares statistic for assessing the goodness-of-fit of the logistic multilevel models. We perform a simulation study to evaluate the type I error rate and the power performance for detecting a missing quadratic or interaction term of fixed effects using the kernel smoothed unweighted sum of squares statistic based on the local polynomial smoothed residuals over x-space. We also use a real data set in clinical trials to illustrate this application.
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Lin, Chi-Chen, and 林其臻. "Applications of linear mixed model and generalized estimating equation to biological control data." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/99609338700121243103.
Full textAbstract:
碩士國立臺灣大學
農藝學研究所
96
Longitudinal data can be obtained by observing same object at different time. So the observations are not indepedent with each other.In drug experiment,observations on the reactions of the same patient at different time are not mutually independent. In agriculture,the effect of fertilizers or insecticides can be treated as longitudinal data too, especially for perennial crops. Because of the existence of correlations between observations,it is not appropriate to use general regression analysis or ANOVA.Linear mixed model and generalized estimating equation are two kinds of methods often used in analyzing longitudinal data. Generalized estimating equation divides data into different clusters by their correlations.Then it can be analyzed by general regression analysis ,assuming that the clusters are independent with one another.Linear mixed model is used more often,because the model can be used to analyze the data with fixed and random effect at the same time. The data used in the thesis was provided by Biological Control Laboratory in Department of Plant Protection in National PingTung University of Science & Technology.The main interest is to know the effect of Trichoderma spp. on Rhizoctonia solani with repeated measure data. Chan(2003) analyzed the data by using the method of Biological assay.This thesis analyzes the same data by using linear mixed model and generalized estimating equation.The effect of seven species of Trichoderma spp. is fixed effect and the effect of observations from repeat measurement is random effect.The results are also compared with those obtained by Chan(2003).
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McIntosh, Avery Isaac. "Extensions to Bayesian generalized linear mixed effects models for household tuberculosis transmission." Thesis, 2017. https://hdl.handle.net/2144/22451.
Full textAbstract:
Understanding tuberculosis transmission is vital for efforts at interrupting the spread of disease. Household contact studies that follow persons sharing a household with a TB case—so-called household contacts—and test for latent TB infection by tuberculin skin test conversion give investigators vital information about risk factors for TB transmission. In these studies, investigators often assume secondary cases are infected by the primary TB case, despite substantial evidence that infection from a source outside the home is often equally likely, especially in high-prevalence settings. Investigators may discard information on contacts who test positive at study initiation due to uncertainty of the infection source, or assume infected contacts were infected from the index case prior to study initiation. With either assumption, information on transmission dynamics is lost or incomplete, and estimates of household risk factors for transmission will be biased. This dissertation describes an approach to modeling TB transmission that accounts for community-acquired transmission in the estimation of transmission risk factors from household contact study data. The proposed model generates population-specific estimates of the probability a contact of an infectious case will be infected from a source outside the home—a vital statistic for planning effective interventions to halt disease spread—in additional to estimates of household transmission predictors. We first describe the model analytically, and then apply it to synthetic datasets under different risk scenarios. We then fit the model to data taken from three household contact studies in different locations: Brazil, India, and Uganda. Infection predictors such as contact sleeping proximity to the index case and index case disease severity are underestimated in standard models compared to the proposed method, and non-household TB infection risk increases with age stratum, reflecting longer at-risk duration for community-based exposure for older contacts. This analysis will aid public health planners in understanding how best to interrupt TB spread in disparate populations by characterizing where transmission risk is greatest and which risk factors influence household-acquired transmission. Finally, we present an open-source software package in the R environment titled upmfit for modular implementation of the Bayesian Markov Chain Monte Carlo methods used to estimate the model.2018-05-10T00:00:00Z
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Yu, Chia-Lun, and 余佳倫. "On the Estimation Methods for the Generalized Linear Mixed Effect Model with Measurement Error." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/52245028576933214937.
Full textAbstract:
碩士淡江大學
數學學系碩士班
100
When the measurement error and mixed effect appear in the model at the same time, we can not find much discussion on the literature. The main reason is that the marginal distribution of the integral to the random effect is no longer a generalized linear model. This paper discussed the estimated method between measurement error and mixed effect in the log-linear and logistic model. In the log-linear model, the estimation method usually included naive, regression calibration, simulation extrapolation, small measurement error approximation, and there is another estimation method "Weighted and Corrected Score Function" which is weighted, corrected and weighted again under replication situation. The logistic model in addition to use the integral to obtain the marginal distribution, it also used the moment constructed estimated equation to estimate and compared between partial calibration and without calibration under replication situation. At last, it used the computer to simulate the estimated method which was brought up in this paper.
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Li, Erning. "Estimation for generalized linear models when covariates are subject-specific parameters in a mixed model for longitudinal measurements." 2004. http://www.lib.ncsu.edu/theses/available/etd-05072004-023712/unrestricted/etd.pdf.
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"Optimal Experimental Designs for Mixed Categorical and Continuous Responses." Doctoral diss., 2017. http://hdl.handle.net/2286/R.I.45584.
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abstract: This study concerns optimal designs for experiments where responses consist of both binary and continuous variables. Many experiments in engineering, medical studies, and other fields have such mixed responses. Although in recent decades several statistical methods have been developed for jointly modeling both types of response variables, an effective way to design such experiments remains unclear. To address this void, some useful results are developed to guide the selection of optimal experimental designs in such studies. The results are mainly built upon a powerful tool called the complete class approach and a nonlinear optimization algorithm. The complete class approach was originally developed for a univariate response, but it is extended to the case of bivariate responses of mixed variable types. Consequently, the number of candidate designs are significantly reduced. An optimization algorithm is then applied to efficiently search the small class of candidate designs for the D- and A-optimal designs. Furthermore, the optimality of the obtained designs is verified by the general equivalence theorem. In the first part of the study, the focus is on a simple, first-order model. The study is expanded to a model with a quadratic polynomial predictor. The obtained designs can help to render a precise statistical inference in practice or serve as a benchmark for evaluating the quality of other designs.Dissertation/Thesis
Doctoral Dissertation Statistics 2017
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Siegle, Micha Benjamin. "Einfluss von transkraniellen Wechselstromstimulationen im Thetabereich auf die Bearbeitung der Stroop-Aufgabe." Doctoral thesis, 2021. http://hdl.handle.net/21.11130/00-1735-0000-0005-15A1-D.
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Li, Li. "Model Selection via Minimum Description Length." Thesis, 2011. http://hdl.handle.net/1807/31834.
Full textAbstract:
The minimum description length (MDL) principle originated from data compression literature and has been considered for deriving statistical model selection procedures. Most existing methods utilizing the MDL principle focus on models consisting of independent data, particularly in the context of linear regression. The data considered in this thesis are in the form of repeated measurements, and the exploration of MDL principle begins with classical linear mixed-effects models. We distinct two kinds of research focuses: one concerns the population parameters and the other concerns the cluster/subject parameters. When the research interest is on the population level, we propose a class of MDL procedures which incorporate the dependence structure within individual or cluster with data-adaptive penalties and enjoy the advantages of Bayesian information criteria. When the number of covariates is large, the penalty term is adjusted by data-adaptive structure to diminish the under selection issue in BIC and try to mimic the behaviour of AIC. Theoretical justifications are provided from both data compression and statistical perspectives. Extensions to categorical response modelled by generalized estimating equations and functional data modelled by functional principle components are illustrated. When the interest is on the cluster level, we use group LASSO to set up a class of candidate models. Then we derive a MDL criterion for this LASSO technique in a group manner to selection the final model via the tuning parameters. Extensive numerical experiments are conducted to demonstrate the usefulness of the proposed MDL procedures on both population level and cluster level.
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"Three Essays on Comparative Simulation in Three-level Hierarchical Data Structure." Doctoral diss., 2017. http://hdl.handle.net/2286/R.I.46248.
Full textAbstract:
abstract: Though the likelihood is a useful tool for obtaining estimates of regression parameters, it is not readily available in the fit of hierarchical binary data models. The correlated observations negate the opportunity to have a joint likelihood when fitting hierarchical logistic regression models. Through conditional likelihood, inferences for the regression and covariance parameters as well as the intraclass correlation coefficients are usually obtained. In those cases, I have resorted to use of Laplace approximation and large sample theory approach for point and interval estimates such as Wald-type confidence intervals and profile likelihood confidence intervals. These methods rely on distributional assumptions and large sample theory. However, when dealing with small hierarchical datasets they often result in severe bias or non-convergence. I present a generalized quasi-likelihood approach and a generalized method of moments approach; both do not rely on any distributional assumptions but only moments of response. As an alternative to the typical large sample theory approach, I present bootstrapping hierarchical logistic regression models which provides more accurate interval estimates for small binary hierarchical data. These models substitute computations as an alternative to the traditional Wald-type and profile likelihood confidence intervals. I use a latent variable approach with a new split bootstrap method for estimating intraclass correlation coefficients when analyzing binary data obtained from a three-level hierarchical structure. It is especially useful with small sample size and easily expanded to multilevel. Comparisons are made to existing approaches through both theoretical justification and simulation studies. Further, I demonstrate my findings through an analysis of three numerical examples, one based on cancer in remission data, one related to the China’s antibiotic abuse study, and a third related to teacher effectiveness in schools from a state of southwest US.Dissertation/Thesis
Doctoral Dissertation Statistics 2017
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Hoque, Md Erfanul. "Longitudinal data analysis with covariates measurement error." 2017. http://hdl.handle.net/1993/31988.
Full textAbstract:
Longitudinal data occur frequently in medical studies and covariates measured by
error are typical features of such data. Generalized linear mixed models (GLMMs)
are commonly used to analyse longitudinal data. It is typically assumed that
the random effects covariance matrix is constant across the subject (and among
subjects) in these models. In many situations, however, this correlation structure
may differ among subjects and ignoring this heterogeneity can cause the biased estimates
of model parameters. In this thesis, following Lee et al. (2012), we propose
an approach to properly model the random effects covariance matrix based on covariates
in the class of GLMMs where we also have covariates measured by error.
The resulting parameters from this decomposition have a sensible interpretation
and can easily be modelled without the concern of positive definiteness of the
resulting estimator. The performance of the proposed approach is evaluated through
simulation studies which show that the proposed method performs very well
in terms biases and mean square errors as well as coverage rates. The proposed
method is also analysed using a data from Manitoba Follow-up Study.February 2017
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Otava, Martin. "Metody výpočtu maximálně věrohodných odhadů v zobecněném lineárním smíšeném modelu." Master's thesis, 2011. http://www.nusl.cz/ntk/nusl-300455.
Full textAbstract:
of the diploma thesis Title: Computational Methods for Maximum Likelihood Estimation in Generalized Linear Mixed Models Author: Bc. Martin Otava Department: Department of Probability and Mathematical Statistics Supervisor: RNDr. Arnošt Komárek, Ph.D., Department of Probability and Mathematical Statistics Abstract: Using maximum likelihood method for generalized linear mixed models, the analytically unsolvable problem of maximization can occur. As solution, iterative and ap- proximate methods are used. The latter ones are core of the thesis. Detailed and general introducing of the widely used methods is emphasized with algorithms useful in practical cases. Also the case of non-gaussian random effects is discussed. The approximate methods are demonstrated using the real data sets. Conclusions about bias and consistency are supported by the simulation study. Keywords: generalized linear mixed model, penalized quasi-likelihood, adaptive Gauss- Hermite quadrature 1