Practical Nonparametric and Semiparametric Bayesian Statistics

1. Front Matter

   Pages i-xvi

   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

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.

• Survival analysis
• bayesian statistics
• linear optimization
• neural networks
• statistics

• 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

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