Springer Texts in Statistics

Generalized Linear Models With Examples in R

Authors: Dunn, Peter, Smyth, Gordon

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  • This book eases students into GLMs and demonstrates the need for GLMs by starting with regression
  • Shows how to implement the principles in R
  • Clearly written and logically structured to aid understanding
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eBook $84.99
price for USA in USD (gross)
  • ISBN 978-1-4419-0118-7
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $109.99
price for USA in USD
  • ISBN 978-1-4419-0117-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this Textbook

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.


The book is designed with the student in mind, making it suitable for self-study or a structured course. Beginning with an introduction to linear regression, the book also devotes time to advanced topics not typically included in introductory textbooks. It features chapter introductions and summaries, clear examples, and many practice problems, all carefully designed to balance theory and practice. The text also provides a working knowledge of applied statistical practice through the extensive use of R, which is integrated into the text. 


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

About the authors

Peter K. Dunn is Associate Professor in the Faculty of Science, Health, Education and Engineering at the University of the Sunshine Coast. His work focuses on mathematical statistics, in particular generalized linear models. He has developed methods for accurate numerical evaluation of the densities of the Tweedie distributions, leading to a better understanding of these distributions. An engaging teacher, Dunn is the recipient of an Australian Office of Learning and Teaching citation. He has also won several conference paper prizes, including the EJ Pitman Prize at the Australian Statistics Conference.  He is a member of the Statistical Society of Australia Inc. and the Australian Mathematics Society. 

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.

Table of contents (13 chapters)

Table of contents (13 chapters)

Buy this book

eBook $84.99
price for USA in USD (gross)
  • ISBN 978-1-4419-0118-7
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $109.99
price for USA in USD
  • ISBN 978-1-4419-0117-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Generalized Linear Models With Examples in R
Authors
Series Title
Springer Texts in Statistics
Copyright
2018
Publisher
Springer-Verlag New York
Copyright Holder
Springer Science+Business Media, LLC, part of Springer Nature
eBook ISBN
978-1-4419-0118-7
DOI
10.1007/978-1-4419-0118-7
Hardcover ISBN
978-1-4419-0117-0
Series ISSN
1431-875X
Edition Number
1
Number of Pages
XX, 562
Number of Illustrations
115 b/w illustrations
Topics