Table of contents

  1. Front Matter
    Pages i-xix
  2. Jon Wakefield
    Pages 1-24
  3. Inferential Approaches

    1. Front Matter
      Pages 25-25
    2. Jon Wakefield
      Pages 27-83
    3. Jon Wakefield
      Pages 85-151
    4. Jon Wakefield
      Pages 153-191
  4. Independent Data

    1. Front Matter
      Pages 193-193
    2. Jon Wakefield
      Pages 195-252
    3. Jon Wakefield
      Pages 253-303
    4. Jon Wakefield
      Pages 305-350
  5. Dependent Data

    1. Front Matter
      Pages 351-351
    2. Jon Wakefield
      Pages 353-423
    3. Jon Wakefield
      Pages 425-500
  6. Nonparametric Modeling

    1. Front Matter
      Pages 501-501
    2. Jon Wakefield
      Pages 503-545
    3. Jon Wakefield
      Pages 547-595
  7. Appendices

    1. Front Matter
      Pages 647-647
    2. Jon Wakefield
      Pages 649-651
    3. Jon Wakefield
      Pages 653-654
    4. Jon Wakefield
      Pages 655-655
    5. Jon Wakefield
      Pages 667-667
    6. Jon Wakefield
      Pages 669-671
    7. Jon Wakefield
      Pages 673-674
  8. Back Matter
    Pages 675-697

About this book


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.


Bayes Frequentist Methods Inference Modeling Regression Analysis

Authors and affiliations

  • Jon Wakefield
    • 1
  1. 1.Department of Statistics & BiostatisticsUniversity of WashingtonSeattleUSA

Bibliographic information

  • DOI
  • Copyright Information Springer Science+Business Media New York 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4419-0924-4
  • Online ISBN 978-1-4419-0925-1
  • Series Print ISSN 0172-7397
  • Buy this book on publisher's site
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