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Alnaji, Lulah A. "Generalized Estimating Equations for Mixed Models". Bowling Green State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1530292694012892.
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Huang, Danwei. "Robustness of generalized estimating equations in credibility models". Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B38842312.
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Huang, Danwei y 黃丹薇. "Robustness of generalized estimating equations in credibility models". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38842312.
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Cai, Jianwen. "Generalized estimating equations for censored multivariate failure time data /". Thesis, Connect to this title online; UW restricted, 1992. http://hdl.handle.net/1773/9581.
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Jang, Mi Jin. "Working correlation selection in generalized estimating equations". Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/2719.
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Longitudinal data analysis is common in biomedical research area. Generalized estimating equations (GEE) approach is widely used for longitudinal marginal models. The GEE method is known to provide consistent regression parameter estimates regardless of the choice of working correlation structure, provided the square root of n consistent nuisance parameters are used. However, it is important to use the appropriate working correlation structure in small samples, since it improves the statistical efficiency of β estimate. Several working correlation selection criteria have been proposed (Rotnitzky and Jewell, 1990; Pan, 2001; Hin and Wang, 2009; Shults et. al, 2009). However, these selection criteria have the same limitation in that they perform poorly when over-parameterized structures are considered as candidates. In this dissertation, new working correlation selection criteria are developed based on generalized eigenvalues. A set of generalized eigenvalues is used to measure the disparity between the bias-corrected sandwich variance estimator under the hypothesized working correlation matrix and the model-based variance estimator under a working independence assumption. A summary measure based on the set of the generalized eigenvalues provides an indication of the disparity between the true correlation structure and the misspecified working correlation structure. Motivated by the test statistics in MANOVA, three working correlation selection criteria are proposed: PT (Pillai's trace type criterion),WR (Wilks' ratio type criterion) and RMR (Roy's Maximum Root type criterion). The relationship between these generalized eigenvalues and the CIC measure is revealed.
In addition, this dissertation proposes a method to penalize for the over-parameterized working correlation structures. The over-parameterized structure converges to the true correlation structure, using extra parameters. Thus, the true correlation structure and the over-parameterized structure tend to provide similar variance estimate of the estimated β and similar working correlation selection criterion values. However, the over-parameterized structure is more likely to be chosen as the best working correlation structure by "the smaller the better" rule for criterion values. This is because the over-parameterization leads to the negatively biased sandwich variance estimator, hence smaller selection criterion value. In this dissertation, the over-parameterized structure is penalized through cluster detection and an optimization function. In order to find the group ("cluster") of the working correlation structures that are similar to each other, a cluster detection method is developed, based on spacings of the order statistics of the selection criterion measures. Once a cluster is found, the optimization function considering the trade-off between bias and variability provides the choice of the "best" approximating working correlation structure.
The performance of our proposed criterion measures relative to other relevant criteria (QIC, RJ and CIC) is examined in a series of simulation studies.
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Clark, Seth K. "Model Robust Regression Based on Generalized Estimating Equations". Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/26588.
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One form of model robust regression (MRR) predicts mean response as a convex
combination of a parametric and a nonparametric prediction. MRR is a semiparametric
method by which an incompletely or an incorrectly specified parametric model can be
improved through adding an appropriate amount of a nonparametric fit. The combined
predictor can have less bias than the parametric model estimate alone and less
variance than the nonparametric estimate alone. Additionally, as shown in previous
work for uncorrelated data with linear mean function, MRR can converge faster than the
nonparametric predictor alone. We extend the MRR technique to the problem of
predicting mean response for clustered non-normal data. We combine a nonparametric
method based on local estimation with a global, parametric generalized estimating
equations (GEE) estimate through a mixing parameter on both the mean scale and the
linear predictor scale. As a special case, when data are uncorrelated, this amounts to
mixing a local likelihood estimate with predictions from a global generalized linear
model. Cross-validation bandwidth and optimal mixing parameter selectors are
developed. The global fits and the optimal and data-driven local and mixed fits are
studied under no/some/substantial model misspecification via simulation. The methods
are then illustrated through application to data from a longitudinal study.Ph. D.
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Akanda, Md Abdus Salam. "A generalized estimating equations approach to capture-recapture closed population models: methods". Doctoral thesis, Universidade de Évora, 2014. http://hdl.handle.net/10174/18297.
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ABSTRACT; Wildlife population parameters, such as capture or detection probabilities, and density or population size, can be estimated from capture-recapture data. These estimates are of particular interest to ecologists and biologists who rely on ac- curate inferences for management and conservation of the population of interest. However, there are many challenges to researchers for making accurate inferences on population parameters. For instance, capture-recapture data can be considered as binary longitudinal observations since repeated measurements are collected on the same individuals across successive points in times, and these observations are often correlated over time. If these correlations are not taken into account when estimating capture probabilities, then parameter estimates will be biased, possibly producing misleading results. Also, an estimator of population size is generally biased under the presence of heterogeneity in capture probabilities. The use of covariates (or auxiliary variables), when available, has been proposed as an alternative way to cope with the problem of heterogeneous capture probabilities. In this dissertation, we are interested in tackling these two main problems, (i) when capture probabilities are dependent among capture occasions in closed population capture-recapture models, and (ii) when capture probabilities are heterogeneous among individuals. Hence, the capture-recapture literature can be improved, if we could propose an approach to jointly account for these problems. In summary, this dissertation proposes: (i) a generalized estimating equations (GEE) approach to model possible effects in capture-recapture closed population studies due to correlation over time and individual heterogeneity; (ii) the corresponding estimating equations for each closed population capture-recapture model; (iii) a comprehensive analysis on various real capture-recapture data sets using classical, GEE and generalized linear mixed models (GLMM); (iv) an evaluation of the effect of ac- counting for correlation structures on capture-recapture model selection based on the ‘Quasi-likelihood Information Criterion (QIC)’; (v) a comparison of the performance of population size estimators using GEE and GLMM approaches in the analysis of capture-recapture data. The performance of these approaches is evaluated by Monte Carlo (MC) simulation studies resembling real capture-recapture data. The proposed GEE approach provides a useful inference procedure for estimating population parameters, particularly when a large proportion of individuals are captured. For a low capture proportion, it is difficult to obtain reliable estimates for all approaches, but the GEE approach outperforms the other methods. Simulation results show that quasi-likelihood GEE provide lower standard error than partial likelihood based on generalized linear modelling (GLM) and GLMM approaches. The estimated population sizes vary on the nature of the existing correlation among capture occasions; RESUMO: Parâmetros populacionais em espécies de vida selvagens, como probabilidade captura ou deteção, e abundância ou densidade da população, podem ser estimados a partir de dados de captura-recaptura. Estas estimativas são de particular interesse para ecologistas e biólogos que dependem de inferências precisas a gestão e conservação das populações. No entanto, há muitos desafios par investigadores fazer inferências precisas de parâmetros populacionais. Por exemplo, os dados de captura-recaptura podem ser considerados como observa longitudinais binárias uma vez que são medições repetidas coletadas nos mesmos indivíduos em pontos sucessivos no tempo, e muitas vezes correlacionadas. Essas correlações não são levadas em conta ao estimar as probabilidades de tura, as estimativas dos parâmetros serão tendenciosas e possivelmente produz resultados enganosos. Também, um estimador do tamanho de uma população geralmente enviesado na presença de heterogeneidade das probabilidades de captura. A utilização de co-variáveis (ou variáveis auxiliares), quando disponível tem sido proposta como uma forma de lidar com o problema de probabilidade captura heterogéneas. Nesta dissertação, estamos interessados em abordar problemas principais em mode1os de captura-recapturar para população fecha (i) quando as probabilidades de captura são dependentes entre ocasiões de captura e (ii) quando as probabilidades de captura são heterogéneas entre os indivíduos Assim, a literatura de captura-recaptura pode ser melhorada, se pudéssemos por uma abordagem conjunta para estes problemas. Em resumo, nesta dissertação propõe-se: (i) uma abordagem de estimação de equações generalizadas (GEE) para modelar possíveis efeitos de correlação temporal e heterogeneidade individual nas probabilidades de captura; (ii) as correspondentes equações de estimação generalizadas para cada modelo de captura-recaptura em população fechadas; (iii) uma análise sobre vários conjuntos de dados reais de captura-recaptura usando a abordagem clássica, GEE e modelos linear generalizados misto (GLMM); (iv) uma avaliação do efeito das estruturas de correlação na seleção de modelos de captura-recaptura com base no ‘critério de informação da Quasi-verossimilhança (QIC); (v) uma comparação da performance das estimativas do tamanho da população usando GEE e GLMM. O desempenho destas abordagens ´e avaliado usando simulações Monte Carlo (MC) que se assemelham a dados de captura- recapture reais. A abordagem GEE proposto ´e um procedimento de inferência útil para estimar parâmetros populacionais, especialmente quando uma grande proporção de indivíduos ´e capturada. Para uma proporção baixa de capturas, ´e difícil obter estimativas fiáveis para todas as abordagens aplicadas, mas GEE supera os outros métodos. Os resultados das simulações mostram que o método da quase-verossimilhança do GEE fornece estimativas do erro padrão menor do que o método da verossimilhança parcial dos modelos lineares generalizados (GLM) e GLMM. As estimativas do tamanho da população variam de acordo com a natureza da correlação existente entre as ocasiões de captura.
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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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Liu, Fangda y 刘芳达. "Two results in financial mathematics and bio-statistics". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B46976437.
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Zheng, Xueying y 郑雪莹. "Robust joint mean-covariance model selection and time-varying correlation structure estimation for dependent data". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hub.hku.hk/bib/B50899703.
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In longitudinal and spatio-temporal data analysis, repeated measurements from a subject can be either regional- or temporal-dependent. The correct specification of the within-subject covariance matrix cultivates an efficient estimation for mean regression coefficients.
In this thesis, robust estimation for the mean and covariance jointly for the regression model of longitudinal data within the framework of generalized estimating equations (GEE) is developed. The proposed approach integrates the robust method and joint mean-covariance regression modeling. Robust generalized estimating equations using bounded scores and leverage-based weights are employed for the mean and covariance to achieve robustness against outliers. The resulting estimators are shown to be consistent and asymptotically normally distributed.
Robust variable selection method in a joint mean and covariance model is considered, by proposing a set of penalized robust generalized estimating equations to estimate simultaneously the mean regression coefficients, the generalized autoregressive coefficients and innovation variances introduced by the modified Cholesky decomposition. The set of estimating equations select important covariate variables in both mean and covariance models together with the estimating procedure. Under some regularity conditions, the oracle property of the proposed robust variable selection method is developed. For these two robust joint mean and covariance models, simulation studies and a hormone data set analysis are carried out to assess and illustrate the small sample performance, which show that the proposed methods perform favorably by combining the robustifying and penalized estimating techniques together in the joint mean and covariance model.
Capturing dynamic change of time-varying correlation structure is both interesting and scientifically important in spatio-temporal data analysis. The time-varying empirical estimator of the spatial correlation matrix is approximated by groups of selected basis matrices which represent substructures of the correlation matrix. After projecting the correlation structure matrix onto the space spanned by basis matrices, varying-coefficient model selection and estimation for signals associated with relevant basis matrices are incorporated. The unique feature of the proposed model and estimation is that time-dependent local region signals can be detected by the proposed penalized objective function. In theory, model selection consistency on detecting local signals is provided. The proposed method is illustrated through simulation studies and a functional magnetic resonance imaging (fMRI) data set from an attention deficit hyperactivity disorder (ADHD) study.published_or_final_version
Statistics and Actuarial Science
Doctoral
Doctor of Philosophy
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Brady, Kaitlyn. "Learning Curves in Emergency Ultrasonography". Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/1150.
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"This project utilized generalized estimating equations and general linear modeling to model learning curves for sonographer performance in emergency ultrasonography. Performance was measured in two ways: image quality (interpretable vs. possible hindrance in interpretation) and agreement of findings between the sonographer and an expert reviewing sonographer. Records from 109 sonographers were split into two data sets-- training (n=50) and testing (n=59)--to conduct exploratory analysis and fit the final models for analysis, respectively. We determined that the number of scans of a particular exam type required for a sonographer to obtain quality images on that exam type with a predicted probability of 0.9 is highly dependent upon the person conducting the review, the indication of the scan (educational or medical), and the outcome of the scan (whether there is a pathology positive finding). Constructing family-wise 95% confidence intervals for each exam type demonstrated a large amount of variation for the number of scans required both between exam types and within exam types. It was determined that a sonographer's experience with a particular exam type is not a significant predictor of future agreement on that exam type and thus no estimates were made based on the agreement learning curves. In addition, we concluded based on a type III analysis that when already considering exam type related experience, the consideration of experience on other exam types does not significantly impact the learning curve for quality. However, the learning curve for agreement is significantly impacted by the additional consideration of experience on other exam types."
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Deng, Wei. "Multiple imputation for marginal and mixed models in longitudinal data with informative missingness". Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1126890027.
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Thesis (Ph. D.)--Ohio State University, 2005.Title from first page of PDF file. Document formatted into pages; contains xiii, 108 p.; also includes graphics. Includes bibliographical references (p. 104-108). Available online via OhioLINK's ETD Center
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Campbell, David Alexander. "Bayesian collocation tempering and generalized profiling for estimation of parameters from differential equation models". Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=103368.
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The widespread use of ordinary differential equation (ODE) models has long been underrepresented in the statistical literature. The most common methods for estimating parameters from ODE models are nonlinear least squares and an MCMC based method. Both of these methods depend on a likelihood involving the numerical solution to the ODE. The challenge faced by these methods is parameter spaces that are difficult to navigate, exacerbated by the wide variety of behaviours that a single ODE model can produce with respect to small changes in parameter values.In this work, two competing methods, generalized profile estimation and Bayesian collocation tempering are described. Both of these methods use a basis expansion to approximate the ODE solution in the likelihood, where the shape of the basis expansion, or data smooth, is guided by the ODE model. This approximation to the ODE, smooths out the likelihood surface, reducing restrictions on parameter movement.
Generalized Profile Estimation maximizes the profile likelihood for the ODE parameters while profiling out the basis coefficients of the data smooth. The smoothing parameter determines the balance between fitting the data and the ODE model, and consequently is used to build a parameter cascade, reducing the dimension of the estimation problem. Generalized profile estimation is described with under a constraint to ensure the smooth follows known behaviour such as monotonicity or non-negativity.
Bayesian collocation tempering, uses a sequence posterior densities with smooth approximations to the ODE solution. The level of the approximation is determined by the value of the smoothing parameter, which also determines the level of smoothness in the likelihood surface. In an algorithm similar to parallel tempering, parallel MCMC chains are run to sample from the sequence of posterior densities, while allowing ODE parameters to swap between chains. This method is introduced and tested against a variety of alternative Bayesian models, in terms of posterior variance and rate of convergence.
The performance of generalized profile estimation and Bayesian collocation tempering are tested and compared using simulated data sets from the FitzHugh-Nagumo ODE system and real data from nylon production dynamics.
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Li, Daoji. "Empirical likelihood and mean-variance models for longitudinal data". Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/empirical-likelihood-and-meanvariance-models-for-longitudinal-data(98e3c7ef-fc88-4384-8a06-2c76107a9134).html.
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Improving the estimation efficiency has always been one of the important aspects in statistical modelling. Our goal is to develop new statistical methodologies yielding more efficient estimators in the analysis of longitudinal data. In this thesis, we consider two different approaches, empirical likelihood and jointly modelling the mean and variance, to improve the estimation efficiency. In part I of this thesis, empirical likelihood-based inference for longitudinal data within the framework of generalized linear model is investigated. The proposed procedure takes into account the within-subject correlation without involving direct estimation of nuisance parameters in the correlation matrix and retains optimality even if the working correlation structure is misspecified. The proposed approach yields more efficient estimators than conventional generalized estimating equations and achieves the same asymptotic variance as quadratic inference functions based methods. The second part of this thesis focus on the joint mean-variance models. We proposed a data-driven approach to modelling the mean and variance simultaneously, yielding more efficient estimates of the mean regression parameters than the conventional generalized estimating equations approach even if the within-subject correlation structure is misspecified in our joint mean-variance models. The joint mean-variances in parametric form as well as semi-parametric form has been investigated. Extensive simulation studies are conducted to assess the performance of our proposed approaches. Three longitudinal data sets, Ohio Children’s wheeze status data (Ware et al., 1984), Cattle data (Kenward, 1987) and CD4+ data (Kaslowet al., 1987), are used to demonstrate our models and approaches.
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Green, Brittany. "Ultra-high Dimensional Semiparametric Longitudinal Data Analysis". University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1593171378846243.
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Kurusu, Ricardo Salles. "Avaliação de técnicas de diagnóstico para a análise de dados com medidas repetidas". Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-21062013-202727/.
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Dentre as possíveis propostas encontradas na literatura estatística para analisar dados oriundos de estudos com observações correlacionadas, estão os modelos condicionais e os modelos marginais. Diversas técnicas têm sido propostas para a análise de diagnóstico nesses modelos. O objetivo deste trabalho é apresentar algumas das técnicas de diagnóstico disponíveis para os dois tipos de modelos e avaliá-las por meio de estudos de simulação. As técnicas apresentadas também foram aplicadas em um conjunto de dados reais.Conditional and marginal models are among the possibilities in statistical literature to analyze data from studies with correlated observations. Several techniques have been proposed for diagnostic analysis in these models. The objective of this work is to present some of the diagnostic techniques available for both modeling approaches and to evaluate them by simulation studies. The presented techniques were also applied in a real dataset.
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Venezuela, Maria Kelly. ""Modelos lineares generalizados para análise de dados com medidas repetidas"". Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-07072006-122612/.
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Neste trabalho, apresentamos as equações de estimação generalizadas desenvolvidas por Liang e Zeger (1986), sob a ótica da teoria de funções de estimação apresentada por Godambe (1991). Essas equações de estimação são obtidas para os modelos lineares generalizados (MLGs) considerando medidas repetidas. Apresentamos também um processo iterativo para estimação dos parâmetros de regressão, assim como testes de hipóteses para esses parâmetros. Para a análise de resíduos, generalizamos para dados com medidas repetidas algumas técnicas de diagnóstico usuais em MLGs. O gráfico de probabilidade meio-normal com envelope simulado é uma proposta para avaliarmos a adequação do ajuste do modelo. Para a construção desse gráfico, simulamos respostas correlacionadas por meio de algoritmos que descrevemos neste trabalho. Por fim, realizamos aplicações a conjuntos de dados reais.In this work, we consider the generalized estimation equations developed by Liang and Zeger (1986) focusing the theory of estimating functions presented by Godambe (1991). These estimation equations are an extension of generalized linear models (GLMs) to the analysis of repeated measurements. We present an iterative procedure to estimate the regression parameters as well as hypothesis testing of these parameters. For the residual analysis, we generalize to repeated measurements some diagnostic methods available for GLMs. The half-normal probability plot with a simulated envelope is useful for diagnosing model inadequacy and detecting outliers. To obtain this plot, we consider an algorithm for generating a set of nonnegatively correlated variables having a specified correlation structure. Finally, the theory is applied to real data sets.
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Qi, Xin. "Socio-environmental factors and suicide in Queensland, Australia". Thesis, Queensland University of Technology, 2009. https://eprints.qut.edu.au/30317/1/Xin_Qi_Thesis.pdf.
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Suicide has drawn much attention from both the scientific community and the public. Examining the impact of socio-environmental factors on suicide is essential in developing suicide prevention strategies and interventions, because it will provide health authorities with important information for their decision-making. However, previous studies did not examine the impact of socio-environmental factors on suicide using a spatial analysis approach.
The purpose of this study was to identify the patterns of suicide and to examine how socio-environmental factors impact on suicide over time and space at the Local Governmental Area (LGA) level in Queensland. The suicide data between 1999 and 2003 were collected from the Australian Bureau of Statistics (ABS). Socio-environmental variables at the LGA level included climate (rainfall, maximum and minimum temperature), Socioeconomic Indexes for Areas (SEIFA) and demographic variables (proportion of Indigenous population, unemployment rate, proportion of population with low income and low education level). Climate data were obtained from Australian Bureau of Meteorology. SEIFA and demographic variables were acquired from ABS. A series of statistical and geographical information system (GIS) approaches were applied in the analysis. This study included two stages. The first stage used average annual data to view the spatial pattern of suicide and to examine the association between socio-environmental factors and suicide over space. The second stage examined the spatiotemporal pattern of suicide and assessed the socio-environmental determinants of suicide, using more detailed seasonal data.
In this research, 2,445 suicide cases were included, with 1,957 males (80.0%) and 488 females (20.0%). In the first stage, we examined the spatial pattern and the determinants of suicide using 5-year aggregated data. Spearman correlations were used to assess associations between variables. Then a Poisson regression model was applied in the multivariable analysis, as the occurrence of suicide is a small probability event and this model fitted the data quite well. Suicide mortality varied across LGAs and was associated with a range of socio-environmental factors. The multivariable analysis showed that maximum temperature was significantly and positively associated with male suicide (relative risk [RR] = 1.03, 95% CI: 1.00 to 1.07). Higher proportion of Indigenous population was accompanied with more suicide in male population (male: RR = 1.02, 95% CI: 1.01 to 1.03). There was a positive association between unemployment rate and suicide in both genders (male: RR = 1.04, 95% CI: 1.02 to 1.06; female: RR = 1.07, 95% CI: 1.00 to 1.16). No significant association was observed for rainfall, minimum temperature, SEIFA, proportion of population with low individual income and low educational attainment.
In the second stage of this study, we undertook a preliminary spatiotemporal analysis of suicide using seasonal data. Firstly, we assessed the interrelations between variables. Secondly, a generalised estimating equations (GEE) model was used to examine the socio-environmental impact on suicide over time and space, as this model is well suited to analyze repeated longitudinal data (e.g., seasonal suicide mortality in a certain LGA) and it fitted the data better than other models (e.g., Poisson model). The suicide pattern varied with season and LGA. The north of Queensland had the highest suicide mortality rate in all the seasons, while there was no suicide case occurred in the southwest. Northwest had consistently higher suicide mortality in spring, autumn and winter. In other areas, suicide mortality varied between seasons. This analysis showed that maximum temperature was positively associated with suicide among male population (RR = 1.24, 95% CI: 1.04 to 1.47) and total population (RR = 1.15, 95% CI: 1.00 to 1.32). Higher proportion of Indigenous population was accompanied with more suicide among total population (RR = 1.16, 95% CI: 1.13 to 1.19) and by gender (male: RR = 1.07, 95% CI: 1.01 to 1.13; female: RR = 1.23, 95% CI: 1.03 to 1.48). Unemployment rate was positively associated with total (RR = 1.40, 95% CI: 1.24 to 1.59) and female (RR=1.09, 95% CI: 1.01 to 1.18) suicide. There was also a positive association between proportion of population with low individual income and suicide in total (RR = 1.28, 95% CI: 1.10 to 1.48) and male (RR = 1.45, 95% CI: 1.23 to 1.72) population. Rainfall was only positively associated with suicide in total population (RR = 1.11, 95% CI: 1.04 to 1.19). There was no significant association for rainfall, minimum temperature, SEIFA, proportion of population with low educational attainment. The second stage is the extension of the first stage. Different spatial scales of dataset were used between the two stages (i.e., mean yearly data in the first stage, and seasonal data in the second stage), but the results are generally consistent with each other.
Compared with other studies, this research explored the variety of the impact of a wide range of socio-environmental factors on suicide in different geographical units. Maximum temperature, proportion of Indigenous population, unemployment rate and proportion of population with low individual income were among the major determinants of suicide in Queensland. However, the influence from other factors (e.g. socio-culture background, alcohol and drug use) influencing suicide cannot be ignored. An in-depth understanding of these factors is vital in planning and implementing suicide prevention strategies.
Five recommendations for future research are derived from this study: (1) It is vital to acquire detailed personal information on each suicide case and relevant information among the population in assessing the key socio-environmental determinants of suicide; (2) Bayesian model could be applied to compare mortality rates and their socio-environmental determinants across LGAs in future research; (3) In the LGAs with warm weather, high proportion of Indigenous population and/or unemployment rate, concerted efforts need to be made to control and prevent suicide and other mental health problems; (4) The current surveillance, forecasting and early warning system needs to be strengthened, to trace the climate and socioeconomic change over time and space and its impact on population health; (5) It is necessary to evaluate and improve the facilities of mental health care, psychological consultation, suicide prevention and control programs; especially in the areas with low socio-economic status, high unemployment rate, extreme weather events and natural disasters.
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Qi, Xin. "Socio-environmental factors and suicide in Queensland, Australia". Queensland University of Technology, 2009. http://eprints.qut.edu.au/30317/.
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Suicide has drawn much attention from both the scientific community and the public. Examining the impact of socio-environmental factors on suicide is essential in developing suicide prevention strategies and interventions, because it will provide health authorities with important information for their decision-making. However, previous studies did not examine the impact of socio-environmental factors on suicide using a spatial analysis approach.
The purpose of this study was to identify the patterns of suicide and to examine how socio-environmental factors impact on suicide over time and space at the Local Governmental Area (LGA) level in Queensland. The suicide data between 1999 and 2003 were collected from the Australian Bureau of Statistics (ABS). Socio-environmental variables at the LGA level included climate (rainfall, maximum and minimum temperature), Socioeconomic Indexes for Areas (SEIFA) and demographic variables (proportion of Indigenous population, unemployment rate, proportion of population with low income and low education level). Climate data were obtained from Australian Bureau of Meteorology. SEIFA and demographic variables were acquired from ABS. A series of statistical and geographical information system (GIS) approaches were applied in the analysis. This study included two stages. The first stage used average annual data to view the spatial pattern of suicide and to examine the association between socio-environmental factors and suicide over space. The second stage examined the spatiotemporal pattern of suicide and assessed the socio-environmental determinants of suicide, using more detailed seasonal data.
In this research, 2,445 suicide cases were included, with 1,957 males (80.0%) and 488 females (20.0%). In the first stage, we examined the spatial pattern and the determinants of suicide using 5-year aggregated data. Spearman correlations were used to assess associations between variables. Then a Poisson regression model was applied in the multivariable analysis, as the occurrence of suicide is a small probability event and this model fitted the data quite well. Suicide mortality varied across LGAs and was associated with a range of socio-environmental factors. The multivariable analysis showed that maximum temperature was significantly and positively associated with male suicide (relative risk [RR] = 1.03, 95% CI: 1.00 to 1.07). Higher proportion of Indigenous population was accompanied with more suicide in male population (male: RR = 1.02, 95% CI: 1.01 to 1.03). There was a positive association between unemployment rate and suicide in both genders (male: RR = 1.04, 95% CI: 1.02 to 1.06; female: RR = 1.07, 95% CI: 1.00 to 1.16). No significant association was observed for rainfall, minimum temperature, SEIFA, proportion of population with low individual income and low educational attainment.
In the second stage of this study, we undertook a preliminary spatiotemporal analysis of suicide using seasonal data. Firstly, we assessed the interrelations between variables. Secondly, a generalised estimating equations (GEE) model was used to examine the socio-environmental impact on suicide over time and space, as this model is well suited to analyze repeated longitudinal data (e.g., seasonal suicide mortality in a certain LGA) and it fitted the data better than other models (e.g., Poisson model). The suicide pattern varied with season and LGA. The north of Queensland had the highest suicide mortality rate in all the seasons, while there was no suicide case occurred in the southwest. Northwest had consistently higher suicide mortality in spring, autumn and winter. In other areas, suicide mortality varied between seasons. This analysis showed that maximum temperature was positively associated with suicide among male population (RR = 1.24, 95% CI: 1.04 to 1.47) and total population (RR = 1.15, 95% CI: 1.00 to 1.32). Higher proportion of Indigenous population was accompanied with more suicide among total population (RR = 1.16, 95% CI: 1.13 to 1.19) and by gender (male: RR = 1.07, 95% CI: 1.01 to 1.13; female: RR = 1.23, 95% CI: 1.03 to 1.48). Unemployment rate was positively associated with total (RR = 1.40, 95% CI: 1.24 to 1.59) and female (RR=1.09, 95% CI: 1.01 to 1.18) suicide. There was also a positive association between proportion of population with low individual income and suicide in total (RR = 1.28, 95% CI: 1.10 to 1.48) and male (RR = 1.45, 95% CI: 1.23 to 1.72) population. Rainfall was only positively associated with suicide in total population (RR = 1.11, 95% CI: 1.04 to 1.19). There was no significant association for rainfall, minimum temperature, SEIFA, proportion of population with low educational attainment. The second stage is the extension of the first stage. Different spatial scales of dataset were used between the two stages (i.e., mean yearly data in the first stage, and seasonal data in the second stage), but the results are generally consistent with each other.
Compared with other studies, this research explored the variety of the impact of a wide range of socio-environmental factors on suicide in different geographical units. Maximum temperature, proportion of Indigenous population, unemployment rate and proportion of population with low individual income were among the major determinants of suicide in Queensland. However, the influence from other factors (e.g. socio-culture background, alcohol and drug use) influencing suicide cannot be ignored. An in-depth understanding of these factors is vital in planning and implementing suicide prevention strategies.
Five recommendations for future research are derived from this study: (1) It is vital to acquire detailed personal information on each suicide case and relevant information among the population in assessing the key socio-environmental determinants of suicide; (2) Bayesian model could be applied to compare mortality rates and their socio-environmental determinants across LGAs in future research; (3) In the LGAs with warm weather, high proportion of Indigenous population and/or unemployment rate, concerted efforts need to be made to control and prevent suicide and other mental health problems; (4) The current surveillance, forecasting and early warning system needs to be strengthened, to trace the climate and socioeconomic change over time and space and its impact on population health; (5) It is necessary to evaluate and improve the facilities of mental health care, psychological consultation, suicide prevention and control programs; especially in the areas with low socio-economic status, high unemployment rate, extreme weather events and natural disasters.
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Menarin, Vinicius. "Modelos estatísticos para dados politômicos nominais em estudos longitudinais com uma aplicação à área agronômica". Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-19042016-091641/.
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Estudos em que a resposta de interesse é uma variável categorizada são bastante comuns nas mais diversas áreas da Ciência. Em muitas situações essa resposta é composta por mais de duas categorias não ordenadas, denominada então de uma variável politômica nominal, e em geral o objetivo do estudo é associar a probabilidade de ocorrência de cada categoria aos efeitos de variáveis explicativas. Ademais, existem tipos especiais de estudos em que os dados são coletados diversas vezes para uma mesma unidade amostral ao longo do tempo, os estudos longitudinais. Estudos assim requerem o uso de modelos estatísticos que considerem em sua formulação algum tipo de estrutura que suporte a dependência que tende a surgir entre observações feitas em uma mesma unidade amostral. Neste trabalho são abordadas duas extensões do modelo de logitos generalizados, usualmente empregado quando a resposta é politômica nominal com observações independentes entre si. A primeira consiste de uma modificação das equações de estimação generalizadas para dados nominais que se utiliza de razões de chances locais para descrever a dependência entre as observações da variável resposta politômica ao longo dos diversos tempos observados. Este tipo de modelo é denominado de modelo marginal. A segunda proposta abordada consiste no modelo de logitos generalizados com a inclusão de efeitos aleatórios no preditor linear, que também leva em conta uma dependência entre as observações. Esta abordagem caracteriza o modelo de logitos generalizados misto. Há diferenças importantes inerentes às interpretações dos modelos marginais e mistos, que são discutidas e que devem ser levadas em consideração na escolha da abordagem adequada. Ambas as propostas são aplicadas em um conjunto de dados proveniente de um experimento da área agronômica realizado em campo, conduzido sob um delineamento casualizado em blocos com esquema fatorial para os tratamentos. O experimento foi acompanhado ao longo de seis estações do ano, caracterizando assim uma estrutura longitudinal, sendo a variável resposta o tipo de vegetação observado no campo (touceiras, plantas invasoras ou espaços vazios). Os resultados encontrados são satisfatórios, embora a dependência presente nos dados não seja tão caracterizada; por meio de testes como da razão de verossimilhanças e de Wald diversas diferenças significativas entre os tratamentos foram encontradas. Ainda, devido às diferenças metodológicas das duas abordagens, o modelo marginal baseado nas equações de estimação generalizadas mostra-se mais adequado para esses dados.Studies where the response is a categorical variable are quite common in many fields of Sciences. In many situations this response is composed by more than two unordered categories characterizing a nominal polytomous outcome and, in general, the aim of the study is to associate the probability of occurrence of each category to the effects of variables. Furthermore, there are special types of study where many measurements are taken over the time for the same sampling unit, called longitudinal studies. Such studies require special statistical models that consider some kind of structure that support the dependence that tends to arise from the repeated measurements for the same sampling unit. This work focuses on two extensions of the baseline-category logit model usually employed in cases when there is a nominal polytomous response with independent observations. The first one consists in a modification of the well-known generalized estimating equations for longitudinal data based on local odds ratios to describe the dependence between the levels of the response over the repeated measurements. This type of model is also known as a marginal model. The second approach adds random effects to the linear predictor of the baseline-category logit model, which also considers a dependence between the observations. This characterizes a baseline-category mixed model. There are substantial differences inherent to interpretations when marginal and mixed models are compared, what should be considered in the choice of the most appropriated approach for each situation. Both methodologies are applied to the data of an agronomic experiment installed under a complete randomized block design with a factorial arrangement for the treatments. It was carried out over six seasons, characterizing the longitudinal structure, and the response is the type of vegetation observed in field (tussocks, weeds or regions with bare ground). The results are satisfactory, even if the dependence found in data is not so strong, and likelihood-ratio and Wald tests point to several differences between treatments. Moreover, due to methodological differences between the two approaches, the marginal model based on generalized estimating equations seems to be more appropriate for this data.
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Wang, Shin Cheng. "Analysis of Zero-Heavy Data Using a Mixture Model Approach". Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/30357.
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The problem of high proportion of zeroes has long been an interest in data analysis and modeling, however, there are no unique solutions to this problem. The solution to the individual problem really depends on its particular situation and the design of the experiment. For example, different biological, chemical, or physical processes may follow different distributions and behave differently. Different mechanisms may generate the zeroes and require different modeling approaches. So it would be quite impossible and inflexible to come up with a unique or a general solution.
In this dissertation, I focus on cases where zeroes are produced by mechanisms that create distinct sub-populations of zeroes. The dissertation is motivated from problems of chronic toxicity testing which has a data set that contains a high proportion of zeroes. The analysis of chronic test data is complicated because there are two different sources of zeroes: mortality and non-reproduction in the data. So researchers have to separate zeroes from mortality and fecundity. The use of mixture model approach which combines the two mechanisms to model the data here is appropriate because it can incorporate the mortality kind of extra zeroes.
A zero inflated Poisson (ZIP) model is used for modeling the fecundity in Ceriodaphnia dubia toxicity test. A generalized estimating equation (GEE) based ZIP model is developed to handle longitudinal data with zeroes due to mortality. A joint estimate of inhibition concentration (ICx) is also developed as potency estimation based on the mixture model approach. It is found that the ZIP model would perform better than the regular Poisson model if the mortality is high. This kind of toxicity testing also involves longitudinal data where the same subject is measured for a period of seven days. The GEE model allows the flexibility to incorporate the extra zeroes and a correlation structure among the repeated measures. The problem of zero-heavy data also exists in environmental studies in which the growth or reproduction rates of multi-species are measured. This gives rise to multivariate data. Since the inter-relationships between different species are imbedded in the correlation structure, the study of the information in the correlation of the variables, which is often accessed through principal component analysis, is one of the major interests in multi-variate data. In the case where mortality influences the variables of interests, but mortality is not the subject of interests, the use of the mixture approach can be applied to recover the information of the correlation structure. In order to investigate the effect of zeroes on multi-variate data, simulation studies on principal component analysis are performed. A method that recovers the information of the correlation structure is also presented.Ph. D.
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Carter, Megan Ann. "Do Childhood Excess Weight and Family Food Insecurity Share Common Risk Factors in the Local Environment? An Examination Using a Quebec Birth Cohort". Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/23801.
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Background: Childhood excess weight and family food insecurity are food-system related public health problems that exist in Canada. Since both relate to issues of food accessibility and availability, which have elements of “place”, they may share common risk factors in the local environment that are amenable to intervention. In this area of research, the literature derives mostly from a US context, and there is a dearth of high quality evidence, specifically from longitudinal studies.
Objectives: The main objectives of this thesis were to examine the adjusted associations between the place factors: material deprivation, social deprivation, social cohesion, disorder, and living location, with change in child BMI Z-score and with change in family food insecurity status in a Canadian cohort of children.
Methods: The Québec Longitudinal Study of Child Development was used to meet the main objectives of this thesis. Response data from six collection cycles (4 – 10 years of age) were used in three main analyses. The first analysis examined change in child BMI Z-score as a function of the place factors using mixed models regression. The second analysis examined change in child BMI Z-score as a function of place factors using group-based trajectory modeling. The third and final analysis examined change in family food insecurity status as a function of the place factors using generalized estimating equations.
Results: Social deprivation, social cohesion and disorder were strongly and positively associated with family food insecurity, increasing the odds by 45-76%. These place factors, on the other hand, were not consistently associated with child weight status. Material deprivation was not important for either outcome, except for a slight positive association in the mixed models analysis of child weight status. Living location was not important in explaining family food insecurity. On the other hand, it was associated with child weight status in both analyses, but the nature of the relationship is still unclear.
Conclusions: Results do not suggest that addressing similar place factors may alleviate both child excess weight and family food insecurity. More high quality longitudinal and experimental studies are needed to clarify relationships between the local environment and child weight status and family food insecurity.
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Rodrigues, José Tenylson Gonçalves. "Análise de dados longitudinais para variáveis binárias". Universidade Federal de São Carlos, 2009. https://repositorio.ufscar.br/handle/ufscar/4531.
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The objective of this work is to present techniques of regression analysis for longitudinal data when the response variable is binary. Initially, there is a review of generalized linear models, marginal models, transition models, mixed models, and logistic regression methods of estimation, which will be necessary for the development of work. In addition to the methods of estimation, some structures of correlation will be studied in an attempt to capture the intra-individual serial dependence over time. These methods were applied in two situations, one where the response variable is continuous and normal distribution, and another when the response variable has the Bernoulli distribution. It was also sought to explore and present techniques for selection of models and diagnostics for the two cases. Finally, an application of the above methodology will be presented using a set of real data.
O objetivo deste trabalho é apresentar técnicas de análise de regressão para dados longitudinais quando a variável resposta é binária. Inicialmente, é feita uma revisão sobre modelos lineares generalizados, modelos marginais, modelos de transição, modelos mistos, regressão logística e métodos de estimação, pois serão necessários para o desenvolvimento do trabalho. Além dos métodos de estimação, algumas estruturas de correlação serão estudadas, na tentativa de captar a dependência serial intra-indivíduo ao longo do tempo. Estes métodos foram aplicados em duas situações; uma quando a variável resposta é contínua, e se assume ter distribuição normal, e a outra quando a variável resposta assume ter distribuição de Bernoulli. Também se procurou pesquisar e apresentar técnicas de seleção de modelos e de diagnósticos para os dois casos. Ao final, uma aplicação com a metodologia pesquisada será apresentada utilizando um conjunto de dados reais.
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Song, Hyunjin. "A Dynamic Longitudinal Examination of Social Networks and Political Behavior: The Moderating Effect of Local Network Properties and Its Implication for Social Influence Processes". The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1427490761.
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Mayo-Bruinsma, Liesha. "Family-centered Care Delivery: Comparing Models of Primary Care Service Delivery in Ontario". Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/19952.
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Family-centered care (FCC) focuses on considering the family in planning/implementing care and is associated with increased patient satisfaction. Little is known about factors that influence FCC. Using linear mixed modeling and Generalized Estimating Equations to analyze data from a cross-sectional survey of primary care practices in Ontario, this study sought to determine whether models of primary care service delivery differ in their provision of FCC and to identify characteristics of primary care practices associated with FCC.
Patient-reported scores of FCC were high, but did not differ significantly among primary care models. After accounting for patient characteristics, practice characteristics were not significantly associated with patient-reported FCC. Provider-reported scores of FCC were significantly higher in Community Health Centres than in Family Health Networks. Higher numbers of nurse practitioners and clinical services on site were associated with higher FCC scores but scores decreased as the number of family physicians at a site increased.
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Zhang, Xiaohong. "Generalized estimating equations for clustered survival data". [Ames, Iowa : Iowa State University], 2006.
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Zhao, Chen. "Evaluating Health Policy Effect with Generalized Linear Model and Generalized Estimating Equation Model". The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1586377218891854.
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Chen, I.-Chen. "Improved Methods and Selecting Classification Types for Time-Dependent Covariates in the Marginal Analysis of Longitudinal Data". UKnowledge, 2018. https://uknowledge.uky.edu/epb_etds/19.
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Generalized estimating equations (GEE) are popularly utilized for the marginal analysis of longitudinal data. In order to obtain consistent regression parameter estimates, these estimating equations must be unbiased. However, when certain types of time-dependent covariates are presented, these equations can be biased unless an independence working correlation structure is employed. Moreover, in this case regression parameter estimation can be very inefficient because not all valid moment conditions are incorporated within the corresponding estimating equations. Therefore, approaches using the generalized method of moments or quadratic inference functions have been proposed for utilizing all valid moment conditions. However, we have found that such methods will not always provide valid inference and can also be improved upon in terms of finite-sample regression parameter estimation. Therefore, we propose a modified GEE approach and a selection method that will both ensure the validity of inference and improve regression parameter estimation.
In addition, these modified approaches assume the data analyst knows the type of time-dependent covariate, although this likely is not the case in practice. Whereas hypothesis testing has been used to determine covariate type, we propose a novel strategy to select a working covariate type in order to avoid potentially high type II error rates with these hypothesis testing procedures. Parameter estimates resulting from our proposed method are consistent and have overall improved mean squared error relative to hypothesis testing approaches.
Finally, for some real-world examples the use of mean regression models may be sensitive to skewness and outliers in the data. Therefore, we extend our approaches from their use with marginal quantile regression to modeling the conditional quantiles of the response variable. Existing and proposed methods are compared in simulation studies and application examples.
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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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Hua, Lei. "Spline-based sieve semiparametric generalized estimating equation for panel count data". Diss., University of Iowa, 2010. https://ir.uiowa.edu/etd/517.
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In this thesis, we propose to analyze panel count data using a spline-based
sieve generalized estimating equation method with a semiparametric proportional mean model E(N(t)|Z) = Λ0(t) eβT0Z. The natural log of the baseline mean function, logΛ0(t), is approximated by a monotone cubic B-spline function. The estimates of regression parameters and spline coefficients are the roots of the spline based sieve generalized estimating equations (sieve GEE). The proposed method avoids assumingany parametric structure of the baseline mean function and the underlying counting process. Selection of an appropriate covariance matrix that represents the true correlation between the cumulative counts improves estimating efficiency.
In addition to the parameters existing in the proportional mean function, the estimation that accounts for the over-dispersion and autocorrelation involves an extra nuisance parameter σ2, which could be estimated using a method of moment proposed by Zeger (1988). The parameters in the mean function are then estimated by solving the pseudo generalized estimating equation with σ2 replaced by its estimate, σ2n. We show that the estimate of (β0,Λ0) based on this two-stage approach is still consistent and could converge at the optimal convergence rate in the nonparametric/semiparametric regression setting. The asymptotic normality of the estimate of β0 is also established. We further propose a spline-based projection variance estimating method and show its consistency.
Simulation studies are conducted to investigate finite sample performance of the sieve semiparametric GEE estimates, as well as different variance estimating methods with different sample sizes. The covariance matrix that accounts for the overdispersion generally increases estimating efficiency when overdispersion is present in the data. Finally, the proposed method with different covariance matrices is applied to a real data from a bladder tumor clinical trial.
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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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Badinger, Harald y Cuaresma Jesus Crespo. "Aggregravity: estimating gravity models from aggregate data". Taylor & Francis, 2015. http://dx.doi.org/10.1080/00036846.2014.1002903.
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This paper considers alternative methods to estimate econometric models based on bilateral data when only aggregate information on the dependent variable is available. Such methods can be used to obtain an indication of the sign and magnitude of bilateral model parameters and, more importantly, to decompose aggregate into bilateral data, which can then be used as proxy variables in further empirical analysis. We perform a Monte Carlo study and carry out a simple real world application using intra-EU trade and capital flows, showing that the methods considered work reasonably well and are worthwhile being considered in the absence of bilateral data. (authors' abstract)
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Badinger, Harald y Cuaresma Jesus Crespo. "Aggregravity: Estimating Gravity Models from Aggregate Data". WU Vienna University of Economics and Business, 2014. http://epub.wu.ac.at/4295/1/wp183.pdf.
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This paper considers alternative methods to estimate econometric models based on bilateral data when only aggregate information on the dependent variable is available. Such methods can be used to obtain an indication of the sign and magnitude of bilateral model parameters and, more importantly, to decompose aggregate into bilateral data, which can then be used as proxy variables in further empirical analysis. We perform a Monte Carlo study and carry out a simple real world application using intra-EU trade and capital flows, showing that the methods considered work reasonably well and are worthwhile being considered in the absence of bilateral data. (authors' abstract)Series: Department of Economics Working Paper Series
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Shin, Janey. "Evaluation of candidate genes in family studies, generalized estimating equations and bootstrap approaches". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0002/MQ40723.pdf.
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Brewer, Ciara. "Using generalized estimating equations with regression splines to improve analysis of butterfly transect data /". St Andrews, 2008. http://hdl.handle.net/10023/488.
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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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Liu, Danping. "Semiparametric methods in generalized linear models for estimating population size and fatality rate". Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B36164598.
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Liu, Danping y 劉丹平. "Semiparametric methods in generalized linear models for estimating population size and fatality rate". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B36164598.
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Valois, Marie-France. "Evaluation of the performance of the generalized estimating equations method for the analysis of crossover designs". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ29805.pdf.
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MacNeill, Stephanie Jan. "A statistical analysis of the recurrence of gestational diabetes by logistic regression and generalized estimating equations". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0008/MQ36504.pdf.
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Wang, Liangliang. "Estimating nonlinear mixed-effects models by the generalized profiling method and its application to pharmacokinetics". Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=18424.
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Several methods with software tools have been developed to estimate nonlinear mixed-effects models. However, fewer have addressed the issue when nonlinear mixed-effects models are implicitly expressed as a set of ordinary differential equations (ODE's) while these ODE's have no closed-form solutions. The main objective of this thesis is to solve this problem based on the framework of the generalized profiling method proposed by Ramsay, Hooker, Campbell, and Cao (2007). Four types of parameters are identified and estimated in a cascaded way by a multiple-level nested optimization. In the outermost level, the smoothing parameter is selected by the criterion of generalized cross-validation (GCV). In the outer level, the structural parameters, including the fixed effects, the variance-covariance matrix for random effects, and the residual variance, are optimized by a criterion based on a first-order Taylor expansion of the nonlinear function. In the middle level, the random effects are optimized by the penalized nonlinear least squares. In the inner level, the coefficients of basis function expansions are optimized by penalized smoothing with the penalty defined by ODE's. Consequently, some types of parameters are expressed as explicit or implicit functions 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 parameters for each level of optimization with gradients and Hessian matrices worked out analytically with the Implicit Function Theorem. Our method, along with MATLAB codes, is tested by estimating several compartment models in pharmacokinetics from both simulated and real data sets. Results are compared with the true values or estimates obtained by the package nlme in R, and it turns out that the generalized profiling method can achieve reasonable estimates without solving ODE's directly.Il n'y a aucune solution de exacte pour beaucoup de modèles non-linéaires à effets mixtes (NLME) exprimés comme un ensemble d'équations ordinaires (ODE) en modèles de compartiment. Cette thèse passe en revue plusieurs méthodes et outils courants de logiciel pour NLME, et explore une nouvelle manière d'estimer des effets mixtes non-linéaires en modèles de compartiment basée sur le cadre de la méthode de profilage généralisée proposée par Ramsay, Hooker, Campbell, et Cao (2007). Quatre types de paramètres sont identifiés et estimés d'en cascade par une optimisation de multiple-niveau: le paramètre regularisateur est choisi par le critère de la contre-vérification généralisée (GCV); les paramètres structuraux, y compris les effets fixes, la matrice de variance-covariance pour les effets aléatoires, et la variance résiduelle sont optimisés par un critère basé sur une expansion de premier ordre de Taylor de fonction non-linéaire ; les effets aléatoires sont optimisés par une methode des moindres carrés non-linéaires pénalisés ; et les coefficients d'expansions de fonction de base sont optimisés par un lissage pénalisé avec la pénalité définie par l'equation differentielle. En conséquence, certains des paramètres sont exprimés en tant que fonctions explicites ou implicites d'autres paramètres. La dimensionnalité de l'espace des paramètres est réduite, et la surface d'optimisation devient plus lisse. L'algorithme de Newton-Raphson est appliqué aux paramètres d'évaluation pour chaque niveau d'optimisation, où le théorème des fonctions implicites est employé couramment pour établir les gradients et les matrices de Hessiennes de facon analytiques. La méthode proposée et des codes de MATLAB sont examinés par des applications à plusieurs modèles de compartiment en pharmacocinétique sur des donnees simulées et vraies. Des résultats sont comparés aux valeurs ou aux évaluations vraies obtenues pa
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Venezuela, Maria Kelly. "Equação de estimação generalizada e influência local para modelos de regressão beta com medidas repetidas". Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-10072008-210246/.
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Utilizando a teoria de função de estimação linear ótima (Crowder, 1987), propomos equações de estimação generalizadas para modelos de regressão beta (Ferrari e Cribari-Neto, 2004) com medidas repetidas. Além disso, apresentamos equações de estimação generalizadas para modelos de regressão simplex baseadas nas propostas de Song e Tan (2000) e Song et al. (2004) e equações de estimação generalizadas para modelos lineares generalizados com medidas repetidas baseadas nas propostas de Artes e Jorgensen (2000) e Liang e Zeger (1986). Todas essas equações de estimação são desenvolvidas sob os enfoques da modelagem da média com homogeneidade da dispersão e da modelagem conjunta da média e da dispersão com intuito de incorporar ao modelo uma possível heterogeneidade da dispersão. Como técnicas de diagnóstico, desenvolvemos uma generalização de algumas medidas de diagnóstico quando abordamos quaisquer equações de estimação definidas tanto para modelagem do parâmetro de posição considerando a homogeneidade do parâmetro de dispersão como para modelagem conjunta dos parâmetros de posição e dispersão. Entre essas medidas, destacamos a proposta da influência local (Cook, 1986) desenvolvida para equações de estimação. Essa medida teve um bom desempenho, em simulações, para destacar corretamente pontos influentes. Por fim, realizamos aplicações a conjuntos de dados reais.Based on the concept of optimum linear estimating equation (Crowder, 1987), we develop generalized estimating equation (GEE) to analyze longitudinal data considering marginal beta regression models (Ferrari and Cribari-Neto, 2004). The GEEs are also presented to marginal simplex models for longitudinal continuous proportional data proposed by Song and Tan (2000) and Song et al. (2004) and to generalized linear models for longitudinal data based on the proposes of Artes and J$\\phi$rgensen (2000) and Liang and Zeger (1986). All of them are developed focusing the assumption of homogeneous dispersion and with varying dispersion. For the diagnostic techniques, we generalize some diagnostic measures for estimating equations to model the position parameter considering an homogeneous dispersion parameter and for joint modelling of position and dispersion parameters to take in account a possible heterogeneous dispersion. Among these measures, we point out the local influence (Cook, 1986) developed to estimating equations. This measure can correctly show influential observations in simulation study. Finally, the theory is applied to real data sets.
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Chatterjee, Nilanjan. "Semiparametric inference based on estimating equations in regression models for two phase outcome dependent sampling /". Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/8959.
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Söderdahl, Fabian y Karl Hammarström. "Measuring the causal effect of air temperature on violent crime". Thesis, Uppsala universitet, Statistiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-243130.
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This thesis aimed to apply the causal framework with potential outcomes to examine the causal effect of air temperature on reported violent crimes in Swedish municipalities. The Generalized Estimating Equations method was used on yearly, monthly and also July only data for the time period 2002-2014. One significant causal effect was established but the majority of the results pointed to there being no causal effect between air temperature and reported violent crimes.
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Hans, Richard P. "Estimating the coefficients in a system of compatible growth and yield equations for Loblolly pine". Thesis, Virginia Polytechnic Institute and State University, 1986. http://hdl.handle.net/10919/94460.
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In this thesis the five equation system of growth and yield equations originally developed by Clutter (1963) is examined. The system is redeveloped algebraically to form a truly algebraically compatible system.
Three methods of estimating the coefficients were examined. In the first method, three of the equations were fitted independently using ordinary least squares; these coefficient estimates were carried through to the other equations. No consideration was given to the relationships that must exist between the equation coefficients in order for the system to be numerically consistent. In the second method the system is first developed algebraically, before any of the coefficients are estimated, resulting in a slightly different system which is truly algebraically compatible. The coefficients were estimated by fitting two of the equations, and using these estimates throughout the rest of the system. The resulting system is both numerically consistent and algebraically compatible. In the final method the relationships between the coefficients that must hold for the system to be compatible were incorporated in the coefficient estimation procedure. Seemingly unrelated regression techniques were used to estimate the coefficients.
The three methods resulted in coefficient estimates that were similar, with seemingly unrelated regression producing the most efficient estimators. Prediction ability of the three methods on independent data show no method as being superior, although the seemingly unrelated regression procedure was able to reduce the total system error best.M.S.
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Diaz, Pedro y Grant Skrepnek. "Marginal Tax Rates and Innovative Activity in the Biotech Sector". The University of Arizona, 2013. http://hdl.handle.net/10150/614244.
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Class of 2013 AbstractSpecific Aims: To assess the association between marginal tax rates (MTR) and innovative output of biotechnology firms. The MTR plays an important role in firms’ financing choices. Assessment of a firm’s tax status may reveal how firms decide on investment policies that affect R&D. Methods: A retrospective database analysis was used. Subjects included were firms within the biotechnology sector with the Standard Industrial Classification code of 2836 from 1980 - 2011. MTR Data was obtained from the S&P Compustat database, and Patent data was obtained from the U.S. Patent and Trademark Office. Changes in MTR’s on outcomes of patents were analyzed by performing an inferential analysis. Generalized estimating equations (GEE) were used, specifically utilizing a GEE regression with a negative binomial distributional family with log link, independent correlation structure and robust standard error variance calculation. Patents were regressed by the lagged change in MTR, after interest deductions. Main Results: The lag years 2 and 5 of the MTR change were statistically significant, (p = 0.031) and (p = 0.026) for each model respectively. Every one unit increase in the change of the MTRs was associated with large and significant drops in patents 78.8% (IRR = 0.212), 90.7% (IRR = 0.093), 92.7% (IRR = 0.073) at year 2 lag and 84.8% (IRR = 0.152), 92.6% (IRR = 0.074) at year 5 lag. Conclusion: An increase in the change of the MTR results in significant drops in patenting activity.
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Wang, Xuesong. "SAFETY ANALYSES AT SIGNALIZED INTERSECTIONS CONSIDERING SPATIAL, TEMPORAL AND SITE CORRELATION". Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3436.
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Statistics show that signalized intersections are among the most dangerous locations of a roadway network. Different approaches including crash frequency and severity models have been used to establish the relationship between crash occurrence and intersection characteristics. In order to model crash occurrence at signalized intersections more efficiently and eventually to better identify the significant factors contributing to crashes, this dissertation investigated the temporal, spatial, and site correlations for total, rear-end, right-angle and left-turn crashes. Using the basic regression model for correlated crash data leads to invalid statistical inference, due to incorrect test statistics and standard errors based on the misspecified variance. In this dissertation, the Generalized Estimating Equations (GEEs) were applied, which provide an extension of generalized linear models to the analysis of longitudinal or clustered data. A series of frequency models are presented by using the GEE with a Negative Binomial as the link function. The GEE models for the crash frequency per year (using four correlation structures) were fitted for longitudinal data; the GEE models for the crash frequency per intersection (using three correlation structures) were fitted for the signalized intersections along corridors; the GEE models were applied for the rear-end crash data with temporal or spatial correlation separately. For right-angle crash frequency, models at intersection, roadway, and approach levels were fitted and the roadway and approach level models were estimated by using the GEE to account for the "site correlation"; and for left-turn crashes, the approach level crash frequencies were modeled by using the GEE with a Negative Binomial link function for most patterns and using a binomial logit link function for the pattern having a higher proportion of zeros and ones in crash frequencies. All intersection geometry design features, traffic control and operational features, traffic flows, and crashes were obtained for selected intersections. Massive data collection work has been done. The autoregression structure is found to be the most appropriate correlation structure for both intersection temporal and spatial analyses, which indicates that the correlation between the multiple observations for a certain intersection will decrease as the time-gap increase and for spatially correlated signalized intersections along corridors the correlation between intersections decreases as spacing increases. The unstructured correlation structure was applied for roadway and approach level right-angle crashes and also for different patterns of left-turn crashes at the approach level. Usually two approaches at the same roadway have a higher correlation. At signalized intersections, differences exist in traffic volumes, site geometry, and signal operations, as well as safety performance on various approaches of intersections. Therefore, modeling the total number of left-turn crashes at intersections may obscure the real relationship between the crash causes and their effects. The dissertation modeled crashes at different levels. Particularly, intersection, roadway, and approach level models were compared for right-angle crashes, and different crash assignment criteria of "at-fault driver" or "near-side" were applied for disaggregated models. It shows that for the roadway and approach level models, the "near-side" models outperformed the "at-fault driver" models. Variables in traffic characteristics, geometric design features, traffic control and operational features, corridor level factor, and location type have been identified to be significant in crash occurrence. In specific, the safety relationship between crash occurrence and traffic volume has been investigated extensively at different studies. It has been found that the logarithm of traffic volumes per lane for the entire intersection is the best functional form for the total crashes in both the temporal and spatial analyses. The studies of right-angle and left-turn crashes confirm the assumption that the frequency of collisions is related to the traffic flows to which the colliding vehicles belong and not to the sum of the entering flows; the logarithm of the product of conflicting flows is usually the most significant functional form in the model. This study found that the left-turn protection on the minor roadway will increase rear-end crash occurrence, while the left-turn protection on the major roadway will reduce rear-end crashes. In addition, left-turn protection reduces Pattern 5 left-turn crashes (left-turning traffic collides with on-coming through traffic) specifically, but it increases Pattern 8 left-turn crashes (left-turning traffic collides with near-side crossing through traffic), and it has no significant effect on other patterns of left-turn crashes. This dissertation also investigated some other factors which have not been considered before. The safety effectiveness of many variables identified in this dissertation is consistent with previous studies. Some variables have unexpected signs and a justification is provided. Injury severity also has been studied for Patterns 5 left-turn crashes. Crashes were located to the approach with left-turning vehicles. The "site correlation" among the crashes occurred at the same approach was considered since these crashes may have similar propensity in crash severity. Many methodologies and applications have been attempted in this dissertation. Therefore, the study has both theoretical and implementational contribution in safety analysis at signalized intersections.Ph.D.
Department of Civil and Environmental Engineering
Engineering and Computer Science
Civil Engineering
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Cassely, Ludovic. "Essais sur la performance sociétale des entreprises dans un contexte international : une approche par la diversité des modèles de capitalisme". Electronic Thesis or Diss., Toulon, 2020. http://www.theses.fr/2020TOUL2001.
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L'entreprise dans une démarche de performance globale et face aux nombreux défis du monde contemporain, doit à la fois concilier des impératifs de rentabilité, de pérennité et de performance mais également devenir "vertueuse" vis à vis du monde qui l'entoure. Cet engagement implique des contraintes en termes d'organisation, de respect de l'environnement mais aussi dans les rapports avec les parties prenantes internes et externes et plus globalement vis à vis de la société.Dans ce contexte, la thèse a pour objectif d’identifier, à l’appui des données sociétales fournies par Vigéo-Eiris (base longitudinale 2004-2015), la diversité des facteurs pouvant expliquer la dynamique des comportements sociétaux sur le long terme dans un contexte international au travers de l’appartenance à un modèle de capitalisme.A l’appui d’un cadre théorique pluraliste, elle tente de répondre à cet objectif au travers de trois questions de recherche qui vont permettre :- D’identifier les déterminants de la performance sociétale sur le long terme grâce à une analyse multi-niveaux ;- De mesurer l’impact de la crise de 2008 sur le niveau de performance sociétale des firmes en analysant le niveau d’implication des entreprises avant, pendant et après cette période ;- D’apprécier la dynamique d’amélioration à long terme des performances sociétales en comparant les résultats des entreprises ayant les meilleures notations sociétales avec celles moins bien notéesIn a global performance approach and in the face of the many challenges of the contemporary world, the company must reconcile the imperatives of profitability, sustainability and performance, but also become "virtuous" with respect to the world around it. This commitment implies constraints in terms of organization, respect for the environment, but also in relations with internal and external stakeholders and more generally with respect to society.In this context, the aim of the thesis is to identify, with the support of societal data provided by Vigéo-Eiris (longitudinal basis 2004-2015), the diversity of factors that can explain the dynamics of societal behaviour in the long term in an international context through belonging to a model of capitalism.With the support of a pluralistic theoretical framework, she tries to answer this objective through three research questions that will allow :- To identify the determinants of societal performance over the long term through a multi-level analysis ;- To measure the impact of the 2008 crisis on the level of societal performance of firms by analyzing the level of involvement of firms before, during and after this period ;- To assess the dynamics of long-term improvement in societal performance by comparing the results of the companies with the best societal ratings with those with lower ratings
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Onnen, Nathaniel J. "Estimation of Bivariate Spatial Data". The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1616243660473062.
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