Bayesian Inference in Wavelet-Based Models

  • Peter Müller
  • Brani Vidakovic

Part of the Lecture Notes in Statistics book series (LNS, volume 141)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Introduction

    1. Brani Vidakovic, Peter Müller
      Pages 1-18
  3. Prior Models-Independent Case

    1. Felix Abramovich, Theofanis Sapatinas
      Pages 33-50
    2. R. Todd Ogden, James D. Lynch
      Pages 67-82
    3. Hugh A. Chipman, Lara J. Wolfson
      Pages 83-94
    4. Paul Yau, Robert Kohn
      Pages 95-108
  4. Decision Theoretic Wavelet Shrinkage

    1. Yazhen Wang
      Pages 109-114
    2. Jérôme Kalifa, Stéphane Mallat
      Pages 115-138
    3. David Leporini, Jean-Christophe Pesquet, Hamid Krim
      Pages 155-172
  5. Prior Models- Dependent Case

  6. Spatial Models

    1. Hsin-Cheng Huang, Noel Cressie
      Pages 203-222
    2. Maarten Jansen, Adhemar Bultheel
      Pages 223-242
    3. Luis Pastor, Angel Rodríguez, David Ríos Insua
      Pages 267-290
  7. Empirical Bayes

    1. Merlise A. Clyde, Edward I. George
      Pages 309-322
  8. Case Studies

  9. Back Matter
    Pages 395-396

About this book


This volume presents an overview of Bayesian methods for inference in the wavelet domain. The papers in this volume are divided into six parts: The first two papers introduce basic concepts. Chapters in Part II explore different approaches to prior modeling, using independent priors. Papers in the Part III discuss decision theoretic aspects of such prior models. In Part IV, some aspects of prior modeling using priors that account for dependence are explored. Part V considers the use of 2-dimensional wavelet decomposition in spatial modeling. Chapters in Part VI discuss the use of empirical Bayes estimation in wavelet based models. Part VII concludes the volume with a discussion of case studies using wavelet based Bayesian approaches. The cooperation of all contributors in the timely preparation of their manuscripts is greatly recognized. We decided early on that it was impor­ tant to referee and critically evaluate the papers which were submitted for inclusion in this volume. For this substantial task, we relied on the service of numerous referees to whom we are most indebted. We are also grateful to John Kimmel and the Springer-Verlag referees for considering our proposal in a very timely manner. Our special thanks go to our spouses, Gautami and Draga, for their support.


Markov model hidden Markov model linear regression modeling statistics time series

Editors and affiliations

  • Peter Müller
    • 1
  • Brani Vidakovic
    • 1
  1. 1.Institute of Statistics and Decision SciencesDuke UniversityDurhamEngland

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag New York, Inc. 1999
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-98885-6
  • Online ISBN 978-1-4612-0567-8
  • Series Print ISSN 0930-0325
  • Buy this book on publisher's site