Bayesian and Frequentist Regression Methods

Introduction.- Frequentist Inference.- Bayesian Inference.- Linear Models.- Binary Data Models.- General Regression Models.

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.

"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)