Authors:
- Provides a balanced, modern summary of Bayesian and frequentist methods for regression analysis
- A book website contains R code to reproduce all of the analyses and figures in the book: http://faculty.washington.edu/jonno/regression-methods.html
- Includes supplementary material: sn.pub/extras
Part of the book series: Springer Series in Statistics (SSS)
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Table of contents (19 chapters)
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Front Matter
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Inferential Approaches
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Front Matter
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Independent Data
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Front Matter
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Dependent Data
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Front Matter
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Nonparametric Modeling
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Front Matter
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Appendices
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Front Matter
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About this book
The two philosophical approaches to regression methodology are featured here as complementary techniques, with theory and data analysis providing supplementary components of the discussion. In particular, methods are illustrated using a variety of data sets. The majority of the data sets are drawn from biostatistics but the techniques are generalizable to a wide range of other disciplines.
Reviews
“JonWakefield’s Bayesian and Frequentist Regression Methods provides an excellent parallel treatment of Frequentist followed by Bayesian approaches to linear, generalised linear, generalised linear mixed and non-parametric regression models. This book is impressive both in terms of its coverage and its contents and is an exceptional resource for students and researchers who have some familiarity with these topics.” (Sanjib Basu, International Statistical Review, Vol. 84 (1), 2016)
"Jon Wakefield’s book Bayesian and Frequentist Regression Methods is an incomparable regression text in that it provides the most comprehensive combination of Bayesian and frequentist methods that exists...The book also discusses a comparison of Bayesian and frequentist approaches in basic inferential procedures, hypothesis testing, variable selection, and general regression modeling...no book expounds the subject in the manner of this book, which provides an extensive and thorough discussionof the regression analysis to reflect recent advances in the field from the two statistical perspectives in terms of methods, implementation, and practical applications." (Taeryon Choi, Journal of Agricultural, Biological, and Environmental Statistics)
“This book is dedicated to describing the Bayesian and frequentist regression methods and to illustrating the use of these methods. … This book could be used for three separate graduate courses: regression methods for independent data; regression methods for dependent data; and nonparametric regression and classification. … the book would be a valuable asset for graduate students, researchers in the area of Bayesian and frequentist methods and an invaluable resource for libraries.” (B. M. Golam Kibria, Mathematical Reviews, January, 2014)
"There are a number of books on applied regression, but connecting the applied principles to theory is a challenge. A related challenge in exposition is to unify thethree goals noted at the beginning of this review. Wakefield’s book is an excellent start." (Andrew Gelman, Statistics in Medicine, 2015)
This book is a gem. It is a unique modern regression book, because it includes both Frequentist and Bayesian methods for many of the data types encountered in modern regression analysis, generally put one after the other, so that readers can learn about and compare the two approaches immediately. Topics go through and beyond nonlinear mixed models. All the methods are motivated by interesting data sets, and there are many other data sets available from the author’s web site. There is both R and WinBUGS code for everything. The writing is unusually clear: philosophically, about the practical problems, about the development of the methods and in the data analysis, and it also has a strong series of exercises. It serves especially well as a textbook, but can also be used as a methods reference book.
-Raymond J. Carroll, Distinguished Professor, Texas A&M University
Arguably the most important development of the statistical discipline over the last 40 years has been itsprogressive de-compartmentalisation. In the 1970s, generalized linear models unified a wide range of statistical methods for analysing independently replicated data. In the 1980s, generalized linear mixed models did much the same for dependent data of various kinds, including spatial, longitudinal and genetic settings. Jon Wakefield’s impressive new book takes this process a stage further by recognising that modern computational developments have made models outside the generalized linear class equally accessible, and by taking a refreshingly pragmatic view of different approaches to inference. The book delivers much more than its title suggests – it could very easily be used as the core-text for a year-long masters course in statistical modelling and inference.-Peter J Diggle, Distinguished University Professor, Lancaster University
Estimating equations, sandwich estimators, Bayesian inference, MCMC and longitudinal models, all in one book. This text is truly unique both for its broad coverage and its pragmatic approach to inference. Wakefield rejects the usual Bayesian-Frequentist divide and instead shows how good data analysis embraces the best of both worlds. To quote the author: "Each of the frequentist and Bayesian approaches have their merits and can often be used in tandem ..." Thorough and clear, this book is a wonderful resource for students and researchers who want a complete and practical understanding of modern regression methods.
-Larry Wasserman, Professor, Department of Statistics and Machine Learning Department, Carnegie Mellon University
Authors and Affiliations
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Department of Statistics & Biostatistics, University of Washington, Seattle, USA
Jon Wakefield
About the author
Jon Wakefield is Professor in the Departments of Statistics and Biostatistics at the University of Washington. His interests lie in biostatistics, epidemiology and genetics and in links between frequentist and Bayesian methods. His work has been published extensively. He received his PhD from the University of Nottingham, and his honors include the Guy Medal in Bronze from the Royal Statistical Society, and he is a Fellow of the American Statistical Association. He has previously been the Chair of the Department of Statistics at the University of Washington.
Bibliographic Information
Book Title: Bayesian and Frequentist Regression Methods
Authors: Jon Wakefield
Series Title: Springer Series in Statistics
DOI: https://doi.org/10.1007/978-1-4419-0925-1
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 2013
Hardcover ISBN: 978-1-4419-0924-4Published: 03 January 2013
Softcover ISBN: 978-1-4939-3862-9Published: 23 August 2016
eBook ISBN: 978-1-4419-0925-1Published: 04 January 2013
Series ISSN: 0172-7397
Series E-ISSN: 2197-568X
Edition Number: 1
Number of Pages: XIX, 697