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An Introduction to Generalized Linear Models
An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical practice.
Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers Normal, Poisson, and Binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods.
Introduces GLMs in a way that enables readers to understand the unifying structure that underpins them
Discusses common concepts and principles of advanced GLMs, including nominal and ordinal regression, survival analysis, non-linear associations and longitudinal analysis
Connects Bayesian analysis and MCMC methods to fit GLMs
Contains numerous examples from business, medicine, engineering, and the social sciences
Provides the example code for R, Stata, and WinBUGS to encourage implementation of the methods
Offers the data sets and solutions to the exercises online
Describes the components of good statistical practice to improve scientific validity and reproducibility of results.
Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons.
Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers Normal, Poisson, and Binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods.
Introduces GLMs in a way that enables readers to understand the unifying structure that underpins them
Discusses common concepts and principles of advanced GLMs, including nominal and ordinal regression, survival analysis, non-linear associations and longitudinal analysis
Connects Bayesian analysis and MCMC methods to fit GLMs
Contains numerous examples from business, medicine, engineering, and the social sciences
Provides the example code for R, Stata, and WinBUGS to encourage implementation of the methods
Offers the data sets and solutions to the exercises online
Describes the components of good statistical practice to improve scientific validity and reproducibility of results.
Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons.
392 pages, ebook
First published February 1, 1990
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Displaying 1 - 3 of 3 reviews
December 23, 2019
Nearly 20 years since I learnt about GLMs with the first edition of this as a text. I just re-read it and while naturally dated with regard to software and recent developments (regularisation etc) it is still an excellent, clear introduction. I haven't seen recent editions but am sure they are good too. Some linear algebra and calculus required.
February 18, 2023
A very simple and good book, which introduce the GLMs in a very easy way. Very recomendable.
October 4, 2014
An entry level for generalized linear models. The books covers all the essential things you should know about GLM but ignores many necessary details for the beginners. It is a good choice for beginners who want a quick survey about GLM.
Displaying 1 - 3 of 3 reviews