<BR>Hardcover: 276 pages<BR>Publisher: Springer; 1 edition (June 20, 1997)<BR>Language: English<BR>ISBN-10: 0387982183<BR>ISBN-13: 978-0387982182
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<P>Book Description<BR>Applying Generalized Linear Models describes how generalized linear modelling procedures can be used for statistical modelling</P>
<P> in many different fields, without becoming lost in problems of statistical inference. Many students, even in relatively advanced statistics courses, </P>
<P>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 </P>
<P>many different areas, such as survival models, time series, and spatial analysis. This book should appeal to applied statisticians and to </P>
<P>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</P>
<P> teaching the fundamental uses of statistical modelling. The reader is assumed to have knowledge of basic statistical principles, </P>
<P>whether from a Bayesian, frequentist, or direct likelihood point of view, and should be familiar at least with the analysis of the simpler </P>
<P>normal linear models, regression and ANOVA. The author is professor in the biostatistics department at Limburgs University, Diepenbeek,</P>
<P> in the social science department at the University of Liège, and in medical statistics at DeMontfort University, Leicester. He is the author of </P>
<P>nine other books.</P>