Generalized Linear Models, Second Edition
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.
What people are saying - Write a review
An outline of generalized linear models
Models for continuous data with constant variance
Models for polytomous data
Models with constant coefficient of variation
Other editions - View all
addition adjustment analysis applied approximation assumed assumption asymptotic binomial Chapter choice coefficient column combinations compared components computed conditional consider constant contrasts corresponding covariates cumulants defined depends derivative described deviance discussed dispersion distribution effect equal equations errors estimates example Exercise experiment expression factor fitted fitted values fixed four function further given gives identical important increase independent individual interest inverse involving known levels linear models linear predictor link function log likelihood logistic marginal matrix maximum mean measure method Normal Note observed obtained occur parameter particular plot Poisson positive possible probabilities proportional random regression residual respect response sample scale score Show shown similar squares standard statistic Suppose Table transformation treatment usually values variables variance variation vector weights zero