An Introduction to Generalized Linear Models
Annette J. Dobson, Adrian G. Barnett
Language: English
Published by Taylor and Francis Ltd, GB, 2018
Series: Book 51 of 126 - Chapman & Hall/CRC Texts in Statistical Science
- Softcover
- New
Seller: Rarewaves.com USA, London, London, United KingdomRarewaves.com USA
AbeBooks seller since June 11, 2025
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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.…
Seller Inventory # LU-9781138741515
- Title
- An Introduction to Generalized Linear Models
- Author
- Annette J. Dobson, Adrian G. Barnett
- Publisher
- Taylor and Francis Ltd, GB
- Publication year
- 2018
- Condition
- New
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1138741515
- ISBN 13
- 9781138741515
- Edition
- 4th Edition
- Item weight
- 648 grams
- Series
- Book 51 of 126: Chapman & Hall/CRC Texts in Statistical Science
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.
"Synopsis" may belong to another edition of this title.
About the Author
Annette J. Dobson is Professor of Biostatistics at the Univesity of Queensland.
Adrian G. Barnett is a professor at the Queensland University of Technology.
"About the title" may belong to another edition of this title.
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