Overview
- This book eases students into GLMs and motivates the need for GLMs by starting with regression.
- A practical working knowledge of good applied statistical practice is developed through the use of these real data sets and numerous case studies
- Each example in the text is cross-referenced with the relevant data set so that readers can load this data to follow the analysis in their own R session.
Part of the book series: Springer Texts in Statistics (STS)
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Table of contents (13 chapters)
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Front Matter
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Back Matter
Keywords
About this book
This textbook presents an introduction to generalized linear models, complete with real-world data sets and practice problems, making it applicable for both beginning and advanced students of applied statistics. Generalized linear models (GLMs) are powerful tools in applied statistics that extend the ideas of multiple linear regression and analysis of variance to include response variables that are not normally distributed. As such, GLMs can model a wide variety of data types including counts, proportions, and binary outcomes or positive quantities.
Other features include:
• Advanced topics such as power variance functions, saddlepoint approximations, likelihood score tests, modified profile likelihood, small-dispersion asymptotics, and randomized quantile residuals
• Nearly 100 data sets in the companion R package GLMsData
• Examples that are cross-referenced to the companion data set, allowing readers to load the data and follow the analysis in their own R session
Reviews
“I congratulate the authors for making an important contribution in this field. … the book represents an excellent and very comprehensible introduction into the world of generalized linear models and is recommended for all readers who are looking for a practical introduction to this topic using R.” (Dominic Edelmann, Biometrical Journal, Vol. 62, 2020)
“The book is targeted at students and notes it is appropriate for graduate students. It is also useful to the junior statistician needing to learn how to work a model they are unfamiliar with. The practicing and experienced statistician can use this as a quick reference for working a model they may have forgotten the specific of.” (James P. Howard II, zbMath 1416.62020, 2019)
Authors and Affiliations
About the authors
Gordon K. Smyth is Head of the Bioinformatics Division at the Walter and Eliza Hall Institute of Medical Research and Honorary Professor of Mathematics & Statistics at The University of Melbourne. He has published research on generalized linear models and statistical computing for over 30 years and is the author of several popular R packages. In recent years, he has particularly promoted the use of generalized linear models to model data from genomic sequencing technologies.
Bibliographic Information
Book Title: Generalized Linear Models With Examples in R
Authors: Peter K. Dunn, Gordon K. Smyth
Series Title: Springer Texts in Statistics
DOI: https://doi.org/10.1007/978-1-4419-0118-7
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Science+Business Media, LLC, part of Springer Nature 2018
Hardcover ISBN: 978-1-4419-0117-0Published: 11 November 2018
eBook ISBN: 978-1-4419-0118-7Published: 10 November 2018
Series ISSN: 1431-875X
Series E-ISSN: 2197-4136
Edition Number: 1
Number of Pages: XX, 562
Number of Illustrations: 115 b/w illustrations
Topics: Statistical Theory and Methods, Statistics and Computing/Statistics Programs