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Practical Nonparametric and Semiparametric Bayesian Statistics

  • Book
  • © 1998

Overview

Editors:
  1. Dipak Dey

    1. Department of Statistics, University of Connecticut, Storrs, USA

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  2. Peter Müller

    1. ISDS, Duke University, Durham, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Debajyoti Sinha

    1. Department of Mathematics, University of New Hampshire, Durham, USA

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Part of the book series: Lecture Notes in Statistics (LNS, volume 133)

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About this book

A compilation of original articles by Bayesian experts, this volume presents perspectives on recent developments on nonparametric and semiparametric methods in Bayesian statistics. The articles discuss how to conceptualize and develop Bayesian models using rich classes of nonparametric and semiparametric methods, how to use modern computational tools to summarize inferences, and how to apply these methodologies through the analysis of case studies.

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Table of contents (20 chapters)

  1. Front Matter

    Pages i-xvi
    Download chapter PDF

  2. I Dirichlet And Related Processes

    1. Computing Nonparametric Hierarchical Models

      • Michael D. Escobar, Mike West
      Pages 1-22

    2. Computational Methods for Mixture of Dirichlet Process Models

      • Steven N. MacEachern
      Pages 23-43

    3. Nonparametric Bayes methods using predictive updating

      • Michael A. Newton, Fernando A. Quintana, Yunlei Zhang
      Pages 45-61

    4. Dynamic Display of Changing Posterior in Bayesian Survival Analysis

      • Hani Doss, B. Narasimhan
      Pages 63-87

    5. Semiparametric Bayesian Methods for Random Effects Models

      • Joseph G. Ibrahim, Kenneth P. Kleinman
      Pages 89-114

    6. Nonparametric Bayesian Group Sequential Design

      • Pantelis K. Vlachos, Alan E. Gelfand
      Pages 115-132

  3. II Modeling Random Functions

    1. Wavelet-Based Nonparametric Bayes Methods

      • Brani Vidakovic
      Pages 133-155

    2. Nonparametric Estimation of Irregular Functions with Independent or Autocorrelated Errors

      • Michael Smith, Robert Kohn
      Pages 157-179

    3. Feedforward Neural Networks for Nonparametric Regression

      • David Ríos Insua, Peter Müller
      Pages 181-193

  4. III Lévy And Related Processes

    1. Survival Analysis Using Semiparametric Bayesian Methods

      • Debajyoti Sinha, Dipak K. Dey
      Pages 195-211

    2. Bayesian Nonparametric and Covariate Analysis of Failure Time Data

      • Purushottam W. Laud, Paul Damien, Adrian F. M. Smith
      Pages 213-225

    3. Simulation of Lévy Random Fields

      • Robert L. Wolpert, Katja Ickstadt
      Pages 227-242

    4. Sampling Methods For Bayesian Nonparametric Inference Involving Stochastic Processes

      • Stephen Walker, Paul Damien
      Pages 243-254

    5. Curve and Surface Estimation Using Dynamic Step Functions

      • Juha Heikkinen
      Pages 255-272

  5. IV Prior Elicitation And Asymptotic Properties

    1. Prior Elicitation for Semiparametric Bayesian Survival Analysis

      • Joseph G. Ibrahim, Debajyoti Sinha
      Pages 273-292

    2. Asymptotic Properties of Nonparametric Bayesian Procedures

      • Larry Wasserman
      Pages 293-304

  6. V Case Studies

    1. Modeling Travel Demand in Portland, Oregon

      • Katja Ickstadt, Robert L. Wolpert, Xuedong Lu
      Pages 305-322

    2. Semiparametric PK/PD Models

      • Peter Müller, Gary Rosner
      Pages 323-337

    3. A Bayesian Model for Fatigue Crack Growth

      • Liping Liu, Elja Arjas
      Pages 339-353
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Editors and Affiliations

  • Department of Statistics, University of Connecticut, Storrs, USA

    Dipak Dey

  • ISDS, Duke University, Durham, USA

    Peter Müller

  • Department of Mathematics, University of New Hampshire, Durham, USA

    Debajyoti Sinha

Bibliographic Information

  • Book Title: Practical Nonparametric and Semiparametric Bayesian Statistics

  • Editors: Dipak Dey, Peter Müller, Debajyoti Sinha

  • Series Title: Lecture Notes in Statistics

  • DOI: https://doi.org/10.1007/978-1-4612-1732-9

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media New York 1998

  • Softcover ISBN: 978-0-387-98517-6Published: 28 May 1998

  • eBook ISBN: 978-1-4612-1732-9Published: 06 December 2012

  • Series ISSN: 0930-0325

  • Series E-ISSN: 2197-7186

  • Edition Number: 1

  • Number of Pages: XVI, 392

  • Topics: Applications of Mathematics

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