Generalized Linear Models, Second Edition

Front Cover
CRC Press, Aug 1, 1989 - Mathematics - 532 pages
The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data. Today, it remains popular for its clarity, richness of content and direct relevance to agricultural, biological, health, engineering, and other applications.

The authors focus on examining the way a response variable depends on a combination of explanatory variables, treatment, and classification variables. They give particular emphasis to the important case where the dependence occurs through some unknown, linear combination of the explanatory variables.

The Second Edition includes topics added to the core of the first edition, including conditional and marginal likelihood methods, estimating equations, and models for dispersion effects and components of dispersion. The discussion of other topics-log-linear and related models, log odds-ratio regression models, multinomial response models, inverse linear and related models, quasi-likelihood functions, and model checking-was expanded and incorporates significant revisions.

Comprehension of the material requires simply a knowledge of matrix theory and the basic ideas of probability theory, but for the most part, the book is self-contained. Therefore, with its worked examples, plentiful exercises, and topics of direct use to researchers in many disciplines, Generalized Linear Models serves as ideal text, self-study guide, and reference.
 

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An academic reference book.

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not easy to understand --> too few explanation in plain words.

Contents

Preface to the first edition xvi
8
An outline of generalized linear models
21
Models for continuous data with constant variance
48
Binary data
98
Models for polytomous data
149
Loglinear models
193
Conditional likelihoods
245
Models with constant coefficient of variation
285
Models with additional nonlinear parameters
372
Model checking
391
Models for survival data
419
Components of dispersion
432
Further topics
455
Appendices
469
References
479
Index of data sets
500

Quasilikelihood functions
323
Joint modelling of mean and dispersion
357

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