Generalized Linear Models / Edition 2

by P. McCullagh

Overview

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.

Product Details

ISBN-13:	9780412317606
Publisher:	Taylor & Francis
Publication date:	08/01/1989
Series:	Chapman & Hall/CRC Monographs on Statistics and Applied Probability , #37
Edition description:	REV
Pages:	532
Product dimensions:	6.00(w) x 9.00(h) x (d)
Age Range:	18 Years

About the Author

P. McCullagh

Table of Contents

Preface, Introduction, Background, The Origins of Generalized Linear Models, Scope of the Rest of the Book, An Outline of Generalized Linear Models, Processes in Model Fitting, The Components of a Generalized Linear Model, Measuring the goodness of Fit, Residuals, An Algorithm for Fitting Generalized Linear Models, Models for Continuous Data with Constant Variance, Introduction, Error Structure, Systematic Component (Linear Predictor), Model Formulae for Linear Predictors, Aliasing, Estimation, Tables as Data, Algorithms for Least Squares, Selection of Covariates, Binary Data , Introduction, Binomial Distribution, Models for Binary Responses, Likelihood functions for Binary Data, Over-Dispersion, Example, Models for Polytomous Data, Introduction, Measurement scales, The Multinomical Distribution, Likelihood Functions, Over-Dispersion, Examples, Log-Linear Models, Introduction, Likelihood Functions, Examples, Log-Linear Models and Multinomial Response Models, Multiple responses, Example, Conditional Likelihoods, Introduction, Marginal and conditional Likelihoods, Hypergeometric Distributions, Some Applications Involving Binary data, Some Aplications Involving Polytomous Data, Models with Constant Coefficient of Variation, Introduction, The Gamma Distribution, Models with Gamma-distributed Observations, Examples, Quasi-Likelihood Functions, Introduction, Independent Observations, Dependent Observations, Optimal Estimating Functions, Optimality Criteria, Extended Quasi-Likelihood, Joint Modelling of Mean and Dispersion, Introduction, Model Specification, Interaction between Mean and Dispersion Effects, Extended Quasi-Likelihood as a Criterion, Adjustments of the Estimating Equations, Joint Optimum Estimating Equations, Example: The Production of Leaf-Springs for Trucks, Models with Additional Non-Linear Parameters, Introduction, Pa

Editorial Reviews

"... an important, useful book, well-written by two authorities in the field..."
-Times Higher Education Supplement
"... an enormous range of work is covered... represents, perhaps, the most important field of research in theoretical and practical statistics. For all statisticians working in this field, the book is essential."
-Short Book Reviews
"... this is a rich book; rich in theory, rich in examples, and rich in a statistical sense. I highly recommend it."
-Biometrics
"... a definitive and unified presentation...by the outstanding experts of this field."
-Statistics
"This is a wonderful book... Reading the book is like listening to a good lecturer. The authors present the material clearly, and they treat the reader with respect. There is a balance between discussion, mathematical presentation of models, and examples."
-Technometrics

"... a complete introduction to the topic in a single monograph... a very readable book that provides the reader with great insight into a vast array of data analysis techniques...
-Siam Review

"... a unique and useful text for intermediate undergraduate teaching."
-THES

From the Publisher

Updated and expanded edition of a text for graduate and advanced undergraduate students of applied statistics. Differs most conspicuously from the 1983 edition in that exercises can now be found at the end of each of the fifteen chapters. Not excessively formal; provides useful discussion of ideas which lie a fairly easy remove from the those basic to the theory of linear models. (NW) Annotation c. Book News, Inc., Portland, OR (booknews.com)

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