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An introduction to generalized linear models

Author: Annette J Dobson; Adrian Barnett
Publisher: Boca Raton, FL : CRC Press, 2018. ©2018
Series: Texts in statistical science.
Edition/Format:   eBook : Document : English : Fourth editionView all editions and formats
Summary:
"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  Read more...
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Genre/Form: Statistics
Electronic books
Additional Physical Format: Print version:
Dobson, Annette J., 1945-
Introduction to generalized linear models.
Boca Raton : CRC Press, Taylor & Francis Group, [2018]
(DLC) 2018002845
(OCoLC)1023814906
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Annette J Dobson; Adrian Barnett
ISBN: 9781315182780 1315182785 9781351726221 1351726226 9781351726214 1351726218
OCLC Number: 1031315481
Description: 1 online resource (xv, 376 pages)
Contents: Introduction --
Model fitting --
Exponential family and generalized linear models --
Estimation --
Inference --
Normal linear models --
Binary variables and logistic regression --
Nominal and ordinal logistic regression --
Poisson regression and log-linear models --
Survival analysis --
Clustered and longitudinal data --
Bayesian analysis --
Markov chain Monte Carlo methods --
Example Bayesian analyses.
Series Title: Texts in statistical science.
Responsibility: Annette J. Dobson and Adrian G. Barnett.

Abstract:

"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 themDiscusses common concepts and principles of advanced GLMs, including nominal and ordinal regression, survival analysis, non-linear associations and longitudinal analysisConnects Bayesian analysis and MCMC methods to fit GLMsContains numerous examples from business, medicine, engineering, and the social sciencesProvides the example code for R, Stata, and WinBUGS to encourage implementation of the methodsOffers the data sets and solutions to the exercises onlineDescribes 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."--Provided by publisher.

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Praise for the Third Edition:Overall, this new edition remains a highly useful and compact introduction to a large number of seemingly disparate regression models. Depending on the background of the Read more...

 
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