Frontiers of Statistical Decision Making and Bayesian Analysis

In Honor of James O. Berger

Editors: Chen, M.-H., Müller, P., Sun, D., Ye, K., Dey, D. (Eds.)

  • A concise update on the topics which are currently the most active areas of Bayesian research
  • Written by experts who are contributing to this research
  • Makes diverse research areas accessible to any reader who is familiar with the basics of the Bayesian approach
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Buy this book

eBook $99.00
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  • ISBN 978-1-4419-6944-6
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  • Immediate eBook download after purchase
Hardcover $129.00
price for USA
  • ISBN 978-1-4419-6943-9
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Softcover $129.00
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  • ISBN 978-1-4899-9201-7
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  • Usually dispatched within 3 to 5 business days.
About this book

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.

About the authors

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.

Reviews

From the reviews:

“The book is a ‘Festschrift’ in honour of Jim Berger’s 60th birthday that was celebrated at a conference in spring 2010 in Texas. … All the papers are written by experts in their fields and represent the current state of the art in Bayesian modelling. … for those who are interested in Bayesian modelling, there are some interesting aspects to be detected. … the book is aimed for advanced researchers in Bayesian analyses.” (Wolfgang Polasek, International Statistical Review, Vol. 79 (3), 2011)

“This collection contains invited papers by statisticians to honor and acknowledge the contributions of James O. Berger to Bayesian statistics. These papers present recent surveys and developments within the area of statistical decision theory and Bayesian statistics and related topics. … Each chapter … provides a detailed treatment of the topic under consideration. … can be useful for graduate students and researchers from diverse fields of statistics and related disciplines. … this edited volume contains a wealth of knowledge, wisdom and information on Bayesian statistics.” (Technometrics, Vol. 53 (2), May, 2011)


Table of contents (14 chapters)

  • Introduction

    Chen, Ming-Hui (et al.)

    Pages 1-30

  • Objective Bayesian Inference with Applications

    Chen, Ming-Hui (et al.)

    Pages 31-68

  • Bayesian Decision Based Estimation and Predictive Inference

    Chen, Ming-Hui (et al.)

    Pages 69-112

  • Bayesian Model Selection and Hypothesis Tests

    Chen, Ming-Hui (et al.)

    Pages 113-155

  • Bayesian Inference for Complex Computer Models

    Chen, Ming-Hui (et al.)

    Pages 157-184

Buy this book

eBook $99.00
price for USA (gross)
  • ISBN 978-1-4419-6944-6
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $129.00
price for USA
  • ISBN 978-1-4419-6943-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $129.00
price for USA
  • ISBN 978-1-4899-9201-7
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Frontiers of Statistical Decision Making and Bayesian Analysis
Book Subtitle
In Honor of James O. Berger
Editors
  • Ming-Hui Chen
  • Peter Müller
  • Dongchu Sun
  • Keying Ye
  • Dipak Dey
Copyright
2010
Publisher
Springer-Verlag New York
Copyright Holder
Springer Science+Business Media
eBook ISBN
978-1-4419-6944-6
DOI
10.1007/978-1-4419-6944-6
Hardcover ISBN
978-1-4419-6943-9
Softcover ISBN
978-1-4899-9201-7
Edition Number
1
Number of Pages
XXIII, 631
Topics