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Mathematical statistics --- Linear models (Statistics) --- Data processing --- GLIM --- GLIM (Computer programs) --- Data processing. --- Linear models (Statistics) - Data processing

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Stochastic processes --- Mathematical statistics --- Linear models (Statistics) --- Modèles linéaires (statistique) --- Processus stochastiques --- Data processing. --- Informatique --- Informatique. --- GLIM --- GLIM (logiciel)

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Applying Generalized Linear Models describes how generalized linear modelling procedures can be used for statistical modelling in many different fields, without becoming lost in problems of statistical inference. Many students, even in relatively advanced statistics courses, do not have an overview whereby they can see that the three areas - linear normal, categorical, and survival models - have much in common. The author shows the unity of many of the commonly used models and provides the reader with a taste of many different areas, such as survival models, time series, and spatial analysis. This book should appeal to applied statisticians and to scientists with a basic grounding in modern statistics. With the many exercises included at the ends of chapters, it will be an excellent text for teaching the fundamental uses of statistical modelling. The reader is assumed to have knowledge of basic statistical principles, whether from a Bayesian, frequentist, or direct likelihood point of view, and should be familiar at least with the analysis of the simpler normal linear models, regression and ANOVA. The author is professor in the biostatistics department at Limburgs University, Diepenbeek, in the social science department at the University of Liège, and in medical statistics at DeMontfort University, Leicester. He is the author of nine other books.

Linear models (Statistics). --- Mathematical statistics --- 519.2 --- Linear models (Statistics) --- Models, Linear (Statistics) --- Mathematical models --- Statistics --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Modèles linéaires (Statistique) --- EPUB-LIV-FT SPRINGER-B --- Mathematics. --- Probabilities. --- Statistics. --- Probability Theory and Stochastic Processes. --- Statistical Theory and Methods. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Distribution (Probability theory. --- Mathematical statistics. --- #PBIB:2005.2 --- Statistics . --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Risk

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Lindsey presents a complete range of material on this subject which can be immediately used with the major statistical packages. The important distinctions between independent and dependent events - frequencies and counts - structures the book. Many of the important areas of modern statistics from generalized linear models to survival analysis are demonstrated to be special cases of categorical data analysis.

Multivariate analysis. --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Mathematical statistics --- Matrices --- Multivariate analysis --- Mathematical statistics. --- Statistique mathématique. --- Analyse multivariée.

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