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Linear models (Statistics) --- Modèles linéaires généralisés --- Computational linguistics. --- Linguistique --- Statistique mathématique --- Informatique

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This book provides a comprehensive treatment on modeling approaches for non-Gaussian repeated measures, possibly subject to incompleteness. The authors begin with models for the full marginal distribution of the outcome vector. This allows model fitting to be based on maximum likelihood principles, immediately implying inferential tools for all parameters in the models. At the same time, they formulate computationally less complex alternatives, including generalized estimating equations and pseudo-likelihood methods. They then briefly introduce conditional models and move on to the random-effects family, encompassing the beta-binomial model, the probit model and, in particular the generalized linear mixed model. Several frequently used procedures for model fitting are discussed and differences between marginal models and random-effects models are given attention The authors consider a variety of extensions, such as models for multivariate longitudinal measurements, random-effects models with serial correlation, and mixed models with non-Gaussian random effects. They sketch the general principles for how to deal with the commonly encountered issue of incomplete longitudinal data. The authors critique frequently used methods and propose flexible and broadly valid methods instead, and conclude with key concepts of sensitivity analysis. Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package. The text is organized so the reader can skip the software-oriented chapters and sections without breaking the logical flow. Geert Molenberghs is Professor of Biostatistics at the Universiteit Hasselt in Belgium and has published methodological work on surrogate markers in clinical trials, categorical data, longitudinal data analysis, and the analysis of non-response in clinical and epidemiological studies. He served as Joint Editor for Applied Statistics (2001-2004) and as Associate Editor for several journals, including Biometrics and Biostatistics. He was President of the International Biometric Society (2004-2005). He was elected Fellow of the American Statistical Association and received the Guy Medal in Bronze from the Royal Statistical Society. Geert Verbeke is Professor of Biostatistics at the Biostatistical Centre of the Katholieke Universiteit Leuven in Belgium. He has published a number of methodological articles on various aspects of models for longitudinal data analyses, with particular emphasis on mixed models. Geert Verbeke is Past President of the Belgian Region of the International Biometric Society, International Program Chair for the International Biometric Conference in Montreal (2006), and Joint Editor of the Journal of the Royal Statistical Society, Series A (2005-2008). He has served as Associate Editor for several journals including Biometrics and Applied Statistics. The authors also wrote a monograph on linear mixed models for longitudinal data (Springer, 2000) and received the American Statistical Association's Excellence in Continuing Education Award, based on short courses on longitudinal and incomplete data at the Joint Statistical Meetings of 2002 and 2004.

Mathematical statistics --- QA 278 Multivariate analysis -- General Works. --- Multivariate analysis --- Longitudinal method --- Statistics --- Statistique mathématique --- Linear models (Statistics) --- Modèles linéaires généralisés. --- #SBIB:303H520 --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Modèles linéaires généralisés --- Statistique mathématique --- Statistique mathematique --- Programmes informatiques

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519.237 --- Linear models (Statistics) --- Multivariate analysis --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Models, Linear (Statistics) --- 519.237 Multivariate statistical methods --- Multivariate statistical methods --- Analysis of variance --- Mathematical statistics --- Matrices --- Mathematical models --- Statistics --- Biomathematics. Biometry. Biostatistics --- Linear models (Statistics). --- Multivariate analysis. --- Modèles linéaires (Statistique) --- Analyse multivariée --- Statistique mathématique --- Modèles linéaires généralisés. --- Modèles linéaires généralisés --- Analyse de régression --- Statistique non paramétrique --- Statistique mathematique --- Analyse multivariee

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Mathematical statistics --- Multivariate analysis --- Analyse multivariée --- 519.237 --- 519.24 --- 303.72 --- 57.087.1 --- #SBIB:303H520 --- AA / International- internationaal --- 303.0 --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Matrices --- Multivariate statistical methods --- Special statistical applications and models --- Vormen van analyse --(sociaal onderzoek) --- Biometry. Statistical study and treatment of biological data --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Statistische technieken in econometrie. Wiskundige statistiek (algemene werken en handboeken). --- Multivariate analysis. --- Methoden en technieken --- statistiek --- statistiek. --- 57.087.1 Biometry. Statistical study and treatment of biological data --- 303.72 Vormen van analyse --(sociaal onderzoek) --- 519.24 Special statistical applications and models --- 519.237 Multivariate statistical methods --- Analyse multivariée --- Statistische technieken in econometrie. Wiskundige statistiek (algemene werken en handboeken) --- Linear models (Statistics) --- Analyse des données --- Modèles linéaires généralisés --- Donnees categorielles --- Statistiek.