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Introduction to Generalized Linear Models

Introduction to Generalized Linear ModelsHeather TurnerESRC National Centre for Research Methods, UKandDepartment of StatisticsUniversity of Warwick, UKWU, 2008 04 22-24 Copyrightc Heather Turner, 2008 Introduction to Generalized Linear ModelsIntroductionThis short course provides an overview of Generalized Linear Models (GLMs).We shall see that these Models extend the Linear modellingframework to variables that are not Normally are most commonly used to model binary or count data, sowe will focus on Models for these types of to Generalized Linear ModelsOutlinesPlanPart I: Introduction to Generalized Linear ModelsPart II: Binary DataPart III: Count DataIntroduction to Generalized Linear ModelsOutlinesPart I: Introduction to Generalized Linear ModelsPart I: IntroductionReview of Linear ModelsGeneralized Linear ModelsGLMs in RExercisesIntroduction to Generalized Linear ModelsOutlinesPart II: Binary DataPart II: Binary DataBinary DataModels for Binary DataModel SelectionModel EvaluationExercisesIntroduction to Generalized Linear ModelsOutlinesPart III: Count DataPart III.

The estimates ^ have the usual properties of maximum likelihood estimators. In particular, ^ is asymptotically N ( ;i 1) where i( ) = 1 X T WX Standard errors for the j may therefore be calculated as the square roots of the diagonal elements of cov^( ^ ) = (X T WX^ ) 1 in which (X T WX^ ) 1 is a by-product of the nal IWLS iteration.

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