Bayesian and Frequentist Regression Methods
By: by Jon Wakefield
Contributors: SpringerLink (Online service)
Series: Springer Series in Statistics
Publisher: Springer New York; Imprint: Springer
Published: 2013
Place of publication: New York, NY
Language: eng
Format: [electronic resource] /
Number of pages: 697
ISBN-13: 9781441909251
Subjects: Mathematical statistics; Statistics; Statistics, general; Statistical Theory and Methods
Description: <i>Bayesian and Frequentist Regression Methods</i> 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.<br>
Table of contents: title: Introduction; title: Frequentist Inference; title: Bayesian Inference; title: Linear Models; title: Binary Data Models; title: General Regression Models.