Frontiers of Statistical Decision Making and Bayesian Analysis

In Honor of James O. Berger

  • Ming-Hui Chen
  • Peter Müller
  • Dongchu Sun
  • Keying Ye
  • Dipak K. Dey

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Ming-Hui Chen, Dipak K. Dey, Peter Müller, Dongchu Sun, Keying Ye
    Pages 1-30
  3. Ming-Hui Chen, Dipak K. Dey, Peter Müller, Dongchu Sun, Keying Ye
    Pages 31-68
  4. Ming-Hui Chen, Dipak K. Dey, Peter Müller, Dongchu Sun, Keying Ye
    Pages 69-112
  5. Ming-Hui Chen, Dipak K. Dey, Peter Müller, Dongchu Sun, Keying Ye
    Pages 113-155
  6. Ming-Hui Chen, Dipak K. Dey, Peter Müller, Dongchu Sun, Keying Ye
    Pages 157-184
  7. Ming-Hui Chen, Dipak K. Dey, Peter Müller, Dongchu Sun, Keying Ye
    Pages 185-217
  8. Ming-Hui Chen, Dipak K. Dey, Peter Müller, Dongchu Sun, Keying Ye
    Pages 219-256
  9. Ming-Hui Chen, Dipak K. Dey, Peter Müller, Dongchu Sun, Keying Ye
    Pages 257-284
  10. Ming-Hui Chen, Dipak K. Dey, Peter Müller, Dongchu Sun, Keying Ye
    Pages 285-325
  11. Ming-Hui Chen, Dipak K. Dey, Peter Müller, Dongchu Sun, Keying Ye
    Pages 327-375
  12. Ming-Hui Chen, Dipak K. Dey, Peter Müller, Dongchu Sun, Keying Ye
    Pages 377-417
  13. Ming-Hui Chen, Dipak K. Dey, Peter Müller, Dongchu Sun, Keying Ye
    Pages 419-466
  14. Ming-Hui Chen, Dipak K. Dey, Peter Müller, Dongchu Sun, Keying Ye
    Pages 467-511
  15. Ming-Hui Chen, Dipak K. Dey, Peter Müller, Dongchu Sun, Keying Ye
    Pages 513-553
  16. Back Matter
    Pages 555-631

About this book

Introduction

Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical and spatio-temporal data analysis and posterior simulation methods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling and applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical and applied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian statistics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians who make use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and research scholars in statistics or biostatistics who wish to acquaint themselves with current research frontiers. Ming-Hui Chen is Professor of Statistics at the University of Connecticut; Dipak K. Dey is Head and Professor of Statistics at the University of Connecticut; Peter Müller is Professor of Biostatistics at the University of Texas M. D. Anderson Cancer Center; Dongchu Sun is Professor of Statistics at the University of Missouri- Columbia; and Keying Ye is Professor of Statistics at the University of Texas at San Antonio.

Keywords

Biostatistics Computer simulation Decision problems Monte Carlo method Objective Bayesian inference STATISTICA bayesian statistics data analysis

Editors and affiliations

  • Ming-Hui Chen
    • 1
  • Peter Müller
    • 2
  • Dongchu Sun
    • 3
  • Keying Ye
    • 4
  • Dipak K. Dey
    • 5
  1. 1.Dept. StatisticsUniversity of ConnecticutStorrsUSA
  2. 2.The University of Texas M. D. Anderson CHoustonUSA
  3. 3.University of Missouri-ColumbiaColumbiaUSA
  4. 4.University of Texas at San AntonioSan AntonioUSA
  5. 5.University of ConnecticutStorrsUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4419-6944-6
  • Copyright Information Springer Science+Business Media, LLC 2010
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4419-6943-9
  • Online ISBN 978-1-4419-6944-6
  • About this book