Bayesian and Frequentist Regression Methods

ISBN-13: 9781493938629 / Angielski / Miękka / 2016 / 697 str.

Jon Wakefield

Opis

This book provides a balanced, modern introduction to Bayesian and frequentist methods for regression analysis. The author discusses Frequentist and Bayesian Inferences; Linear Models; Binary Data Models; General Regression Models and Survival Models.

Recenzje

"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)

Spis treści

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

O autorze

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.

Blurb

Bayesian and Frequentist Regression Methods provides a modern account of both Bayesian and frequentist methods of regression analysis. Many texts cover one or the other of the approaches, but this is the most comprehensive combination of Bayesian and frequentist methods that exists in one place.  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. While the philosophy behind each approach is discussed, the book is not ideological in nature and an emphasis is placed on practical application. It is shown that, in many situations, careful application of the respective approaches can lead to broadly similar conclusions. To use this text, the reader requires a basic understanding of calculus and linear algebra, and introductory courses in probability and statistical theory. The book is based on the author's experience teaching a graduate sequence in regression methods. The book website contains all of the code to reproduce all of the analyses and figures contained in the book.