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Robust Bayesian Analysis

  • Book
  • © 2000

Overview

Editors:
  1. David RĂ­os Insua

    1. ESCET-URJC, Mostoles, Madrid, Spain

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    You can also search for this editor in PubMed   Google Scholar

  2. Fabrizio Ruggeri

    1. CNR IAMI, Milano, Italy

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Statistics (LNS, volume 152)

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Table of contents (21 chapters)

  1. Front Matter

    Pages i-xiii
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  2. Introduction

    1. Front Matter

      Pages 1-1
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    2. Bayesian Robustness

      • James O. Berger, David RĂ­os Insua, Fabrizio Ruggeri
      Pages 1-32

  3. Foundations

    1. Front Matter

      Pages 33-33
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    2. Topics on the Foundations of Robust Bayesian Analysis

      • David RĂ­os Insua, Regino Criado
      Pages 33-44

  4. Global and Local Robustness

    1. Front Matter

      Pages 45-45
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    2. Global Bayesian Robustness for Some Classes of Prior Distributions

      • Elias Moreno
      Pages 45-70

    3. Local Robustness in Bayesian Analysis

      • Paul Gustafson
      Pages 71-88

    4. Global and Local Robustness Approaches: Uses and Limitations

      • Siva Sivaganesan
      Pages 89-108

    5. On the Use of the Concentration Function in Bayesian Robustness

      • Sandra Fortini, Fabrizio Ruggeri
      Pages 109-126

  5. Likelihood Robustness

    1. Front Matter

      Pages 127-127
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    2. Likelihood Robustness

      • N. D. Shyamalkumar
      Pages 127-143

  6. Loss Robustness

    1. Front Matter

      Pages 145-145
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    2. Ranges of Posterior Expected Losses and ε-Robust Actions

      • Dipak K. Dey, Athanasios C. Micheas
      Pages 145-159

    3. Computing Efficient Sets in Bayesian Decision Problems

      • Jacinto MartĂ­n, J. Pablo Arias
      Pages 161-186

    4. Stability of Bayes Decisions and Applications

      • Joseph Kadane, Gabriella Salinetti, Cidambi Srinivasan
      Pages 187-196

  7. Comparison With Other Statistical Methods

    1. Front Matter

      Pages 197-197
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    2. Robustness Issues in Bayesian Model Selection

      • Brunero Liseo
      Pages 197-222

    3. Bayesian Robustness and Bayesian Nonparametrics

      • Sanjib Basu
      Pages 223-240

    4. Γ-Minimax: A Paradigm for Conservative Robust Bayesians

      • Brani Vidakovic
      Pages 241-259
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Keywords

About this book

Robust Bayesian analysis aims at overcoming the traditional objection to Bayesian analysis of its dependence on subjective inputs, mainly the prior and the loss. Its purpose is the determination of the impact of the inputs to a Bayesian analysis (the prior, the loss and the model) on its output when the inputs range in certain classes. If the impact is considerable, there is sensitivity and we should attempt to further refine the information the incumbent classes available, perhaps through additional constraints on and/ or obtaining additional data; if the impact is not important, robustness holds and no further analysis and refinement would be required. Robust Bayesian analysis has been widely accepted by Bayesian statisticians; for a while it was even a main research topic in the field. However, to a great extent, their impact is yet to be seen in applied settings. This volume, therefore, presents an overview of the current state of robust Bayesian methods and their applications and identifies topics of further in­ terest in the area. The papers in the volume are divided into nine parts covering the main aspects of the field. The first one provides an overview of Bayesian robustness at a non-technical level. The paper in Part II con­ cerns foundational aspects and describes decision-theoretical axiomatisa­ tions leading to the robust Bayesian paradigm, motivating reasons for which robust analysis is practically unavoidable within Bayesian analysis.

Editors and Affiliations

  • ESCET-URJC, Mostoles, Madrid, Spain

    David RĂ­os Insua

  • CNR IAMI, Milano, Italy

    Fabrizio Ruggeri

Bibliographic Information

  • Book Title: Robust Bayesian Analysis

  • Editors: David RĂ­os Insua, Fabrizio Ruggeri

  • Series Title: Lecture Notes in Statistics

  • DOI: https://doi.org/10.1007/978-1-4612-1306-2

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media New York 2000

  • Softcover ISBN: 978-0-387-98866-5Published: 14 September 2000

  • eBook ISBN: 978-1-4612-1306-2Published: 06 December 2012

  • Series ISSN: 0930-0325

  • Series E-ISSN: 2197-7186

  • Edition Number: 1

  • Number of Pages: XIII, 422

  • Number of Illustrations: 6 b/w illustrations

  • Topics: Statistical Theory and Methods

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