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Case Studies in Bayesian Statistics

Volume V

  • Constantine Gatsonis
  • Robert E. Kass
  • Bradley Carlin
  • Alicia Carriquiry
  • Andrew Gelman
  • Isabella Verdinelli
  • Mike West

Part of the Lecture Notes in Statistics book series (LNS, volume 162)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Invited Papers

    1. Front Matter
      Pages 1-1
    2. John Barnard, Constantine Frangakis, Jennifer Hill, Donald B. Rubin
      Pages 3-97
    3. Donald A. Berry, Peter Müller, Andy P. Grieve, Michael Smith, Tom Parke, Richard Blazek et al.
      Pages 99-181
    4. Nicola G. Best, Katja Ickstadt, Robert L. Wolpert, Samantha Cockings, Paul Elliott, James Bennett et al.
      Pages 183-259
  3. Contributed Papers

    1. Front Matter
      Pages 261-261
    2. Soledad A. Fernández, Rohan L. Fernando, Alicia L. Carriquiry, Bernt Guldbrandtsen
      Pages 309-328
    3. Peter D. Hoff, Richard B. Halberg, Alexandra Shedlovsky, William F. Dove, Michael A. Newton
      Pages 329-343
    4. Steven N. MacEachern, Mario Peruggia
      Pages 345-362
    5. Scott C. Schmidler, Jun S. Liu, Douglas L. Brutlag
      Pages 363-378
    6. Paola Sebastiani, Marco Ramoni, Paul Cohen
      Pages 379-395
    7. Chin-Pei Tsai, Kathryn Chaloner
      Pages 407-421
  4. Back Matter
    Pages 423-438

About these proceedings

Introduction

The 5th Workshop on Case Studies in Bayesian Statistics was held at the Carnegie Mellon University campus on September 24-25, 1999. As in the past, the workshop featured both invited and contributed case studies. The former were presented and discussed in detail while the latter were presented in poster format. This volume contains the three invited case studies with the accompanying discussion as well as ten contributed pa­ pers selected by a refereeing process. The majority of case studies in the volume come from biomedical research. However, the reader will also find studies in education and public policy, environmental pollution, agricul­ ture, and robotics. INVITED PAPERS The three invited cases studies at the workshop discuss problems in ed­ ucational policy, clinical trials design, and environmental epidemiology, respectively. 1. In School Choice in NY City: A Bayesian Analysis ofan Imperfect Randomized Experiment J. Barnard, C. Frangakis, J. Hill, and D. Rubin report on the analysis of the data from a randomized study conducted to evaluate the New YorkSchool Choice Scholarship Pro­ gram. The focus ofthe paper is on Bayesian methods for addressing the analytic challenges posed by extensive non-compliance among study participants and substantial levels of missing data. 2. In Adaptive Bayesian Designs for Dose-Ranging Drug Trials D. Berry, P. Mueller, A. Grieve, M. Smith, T. Parke, R. Blazek, N.

Keywords

Markov model Odds bayesian statistics hidden Markov model modeling randomized experiment statistics

Editors and affiliations

  • Constantine Gatsonis
    • 1
  • Robert E. Kass
    • 2
  • Bradley Carlin
    • 3
  • Alicia Carriquiry
    • 4
  • Andrew Gelman
    • 5
  • Isabella Verdinelli
    • 6
  • Mike West
    • 7
  1. 1.Center for Statistical SciencesBrown UniversityProvidenceUSA
  2. 2.Department of StatisticsCarnegie Mellon UniversityPittsburghUSA
  3. 3.Division of BiostatisticsUniversity of MinnesotaMinneapolisUSA
  4. 4.Department of StatisticsIowa State UniversityAmesUSA
  5. 5.Department of StatisticsColumbia UniversityNew YorkUSA
  6. 6.Department of StatisticsCarnegie Mellon UniversityPittsburghUSA
  7. 7.Institute of Statistics and Decision SciencesDuke UniversityDurhamUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4613-0035-9
  • Copyright Information Springer-Verlag New York, Inc. 2002
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-95169-0
  • Online ISBN 978-1-4613-0035-9
  • Series Print ISSN 0930-0325
  • Buy this book on publisher's site
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