Bayesian and Frequentist Regression Methods (Springer Series in Statistics) - Tapa dura

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

-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

“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 discussion of 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 the three goals noted at the beginning of this review. Wakefield’s book is an excellent start." (Andrew Gelman, Statistics in Medicine, 2015)