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Linear Models. --- Statistics as Topic. --- Models, Theoretical. --- 519.25 --- #ABIB:astp --- Linear models (Statistics) --- Longitudinal method --- Longitudinal research --- Longitudinal studies --- Methodology --- Research --- Social sciences --- Models, Linear (Statistics) --- Mathematical models --- Mathematical statistics --- Statistics --- 519.25 Statistical data handling --- Statistical data handling --- Area Analysis --- Correlation Studies --- Correlation Study --- Correlation of Data --- Data Analysis --- Estimation Technics --- Estimation Techniques --- Indirect Estimation Technics --- Indirect Estimation Techniques --- Multiple Classification Analysis --- Service Statistics --- Statistical Study --- Statistics, Service --- Tables and Charts as Topic --- Analyses, Area --- Analyses, Data --- Analyses, Multiple Classification --- Analysis, Data --- Analysis, Multiple Classification --- Area Analyses --- Classification Analyses, Multiple --- Classification Analysis, Multiple --- Data Analyses --- Data Correlation --- Data Correlations --- Estimation Technic, Indirect --- Estimation Technics, Indirect --- Estimation Technique --- Estimation Technique, Indirect --- Estimation Techniques, Indirect --- Indirect Estimation Technic --- Indirect Estimation Technique --- Multiple Classification Analyses --- Statistical Studies --- Studies, Correlation --- Studies, Statistical --- Study, Correlation --- Study, Statistical --- Technic, Indirect Estimation --- Technics, Estimation --- Technics, Indirect Estimation --- Technique, Estimation --- Technique, Indirect Estimation --- Techniques, Estimation --- Techniques, Indirect Estimation --- Linear Regression --- Log-Linear Models --- Models, Linear --- Linear Model --- Linear Regressions --- Log Linear Models --- Log-Linear Model --- Model, Linear --- Model, Log-Linear --- Models, Log-Linear --- Regression, Linear --- Regressions, Linear --- Programming --- Experimental Model --- Experimental Models --- Mathematical Model --- Model, Experimental --- Models (Theoretical) --- Models, Experimental --- Models, Theoretic --- Theoretical Study --- Mathematical Models --- Model (Theoretical) --- Model, Mathematical --- Model, Theoretical --- Models, Mathematical --- Studies, Theoretical --- Study, Theoretical --- Theoretical Model --- Theoretical Models --- Theoretical Studies --- Computer Simulation --- Systems Theory --- SAS (Computer file) --- Data processing --- Linear models (Statistics). --- Longitudinal method. --- Data processing. --- QA 279 Analysis of variance and covariance. Experimental design. / General works --- Linear Models --- Statistics as Topic --- Models, Theoretical --- Linear models (Statistics) - Data processing

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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. .

Longitudinal method --- Multivariate analysis --- Academic collection --- 519.22 --- 519.23 --- 57.087.1 --- 681.3*G16 --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Mathematical statistics --- Matrices --- Longitudinal research --- Longitudinal studies --- Methodology --- Research --- Social sciences --- 681.3*G16 Optimization: constrained optimization; gradient methods; integer programming; least squares methods; linear programming; nonlinear programming (Numericalanalysis) --- Optimization: constrained optimization; gradient methods; integer programming; least squares methods; linear programming; nonlinear programming (Numericalanalysis) --- 57.087.1 Biometry. Statistical study and treatment of biological data --- Biometry. Statistical study and treatment of biological data --- 519.23 Statistical analysis. Inference methods --- Statistical analysis. Inference methods --- 519.22 Statistical theory. Statistical models. Mathematical statistics in general --- Statistical theory. Statistical models. Mathematical statistics in general --- #SBIB:303H520 --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Longitudinal method. --- Multivariate analysis. --- Analyse multivariée --- Méthode longitudinale --- EPUB-LIV-FT LIVSTATI SPRINGER-B --- Distribution (Probability theory. --- Mathematical statistics. --- Statistics. --- Probability Theory and Stochastic Processes. --- Statistical Theory and Methods. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities. --- Statistics . --- Probability --- Combinations --- Chance --- Least squares --- Risk

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Mathematical statistics --- Linear models (Statistics) --- Longitudinal method