This book describes how generalised linear modelling procedures can be used in many different fields, without becoming entangled in problems of statistical inference. The author shows the unity of many of the commonly used models and provides readers with a taste of many different areas, such as survival models, time series, and spatial analysis, and of their unity. As such, this book will appeal to applied statisticians and to scientists having a basic grounding in modern statistics. With many exercises at the end of each chapter, it will equally constitute an excellent text for teaching applied statistics students and non statistics majors. The reader is assumed to have knowledge of basic statistical principles, whether from a Bayesian, frequentist, or direct likelihood point of view, being familiar at least with the analysis of the simpler normal linear models, regression and ANOVA.

Generalized linear models have applications in many areas, including social science and life science. This book will serve as a reference and advanced text for students interested in the applications of statistics.

Klappentext

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.

Inhalt
Generalized Linear Modelling: Statistical Modelling.- Exponential Dispersion Models.- Linear Structure.- Three Components of a GLM.- Possible Models.- Inference.- Exercises. Discrete Data: Log Linear Models.- Models of Change.- Overdispersion.- Exercises. Fitting and Comparing Probability Distributions: Fitting Distributions.- Setting Up the Model.- Special Cases.- Exercises. Growth Curves: Exponential Growth Curves.- Logistic Growth Curve.- Gomperz Growth Curve.- More Complex Models.- Exercises. Time Series: Poisson Processes.- Markov Processes.- Repeated Measurements.- Exercises. Survival Data: General Concepts.- 'Nonparametric' Estimation.- Parametric Models.- 'Semiparametric' Models.- Exercises. Event Histories: Event Histories and Survival Distributions.- Counting processes.- Modelling Event Histories.- Generalizations.- Exercises. Spatial data: Spatial Interaction.- Spatial Patterns.- Exercises. Normal Models: Linear Regression.- Analysis of Variance.- Nonlinear Regression.- Exercises. Dynamic Models: Dynamic Generalized Linear Models.- Normal Models.- Count Data.- Positive Response Data.- Continuous Time Nonlinear Models. Appendices: Inference.- Diagnostics.- References.- Index.