Bayesian and frequentist regression methods Jon Wakefield

Format:

Book

Author/Creator:

Wakefield, Jon

Series:

Springer series in statistics

Springer series in statistics 0172-7397

Language:

English

Subjects (All):

Regression analysis--Mathematical models.

Regression analysis.

Bayesian statistical decision theory.

Regression Analysis.

Bayes Theorem.

Medical Subjects:

Regression Analysis.

Bayes Theorem.

Physical Description:

1 online resource

Place of Publication:

New York, NY Springer ©2013

Language Note:

English

System Details:

text file

PDF

Summary:

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

Contents:

Inferential Approaches Frequentist Inference Bayesian Inference Hypothesis Testing and Variable Selection Independent Data Linear Models General Regression Models Binary Data Models Dependent Data Linear Models General Regression Models Nonparametric Modeling Preliminaries for Nonparametric Regression Spline and Kernel Methods Nonparametric Regression with Multiple Predictors

Appendices Differentiation of Matrix Expressions Matrix Results Some Linear Algebra Probability Distributions and Generating Functions Functions of Normal Random Variables Some Results from Classical Statistics Basic Large Sample Theory

Notes:

Includes bibliographical references and index

Other Format:

Printed edition:

ISBN:

9781441909251

1441909257

1441909249

9781441909244

OCLC:

823839429

Access Restriction:

Restricted for use by site license