Frontiers of Statistical Decision Making and Bayesian Analysis In Ho
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 curre
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Ming-Hui Chen · Dipak K. Dey · Peter M¨uller · Dongchu Sun · Keying Ye Editors
Frontiers of Statistical Decision Making and Bayesian Analysis In Honor of James O. Berger
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Editors Prof. Ming-Hui Chen Department of Statistics University of Connecticut 215 Glenbrook Road, U-4120 Storrs, CT 06269 USA [email protected] Prof. Peter M¨uller Department of Biostatistics The University of Texas M. D. Anderson Cancer Center 1515 Holcombe Boulevard, Box 447 Houston, TX 77030 USA [email protected]
Prof. Dipak K. Dey Department of Statistics University of Connecticut 215 Glenbrook Road, U-4120 Storrs, CT 06269 USA [email protected] Prof. Dongchu Sun Department of Statistics University of Missouri-Columbia 146 Middlebush Hall Columbia, MO 65211 USA [email protected]
Prof. Keying Ye Department of Management Science and Statistics, College of Business University of Texas at San Antonio San Antonio, TX 78249 USA [email protected]
ISBN 978-1-4419-6943-9 e-ISBN 978-1-4419-6944-6 DOI 10.1007/978-1-4419-6944-6 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2010931142 c Springer Science+Business Media, LLC 2010 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Dedicated to Jim and Ann Berger for their encouragement, support and love all through our academic life and beyond
Preface
This book surveys the current research frontiers in Bayesian statistics. Over the last decades we have seen an unprecedented explosion of exciting Bayesian work. The explosion is in sheer number of researchers and publications, as well as in the diversity of application areas and research directions. Over the past few years several excellent new introductory texts on Bayesian inference have appeared, as well as specialized monographs that focus on specific aspects of Bayesian inference, including dynamic models, multilevel data, non-parametric Bayes, bioinformatics and many others. Thus this is a natural time for a book that can pull all these diverse areas together and present a snapshot of current research frontiers in Bayesian inference and decision making. The intention of this volume is to provide such a snapshot. Many of the research frontiers that are discussed in this volume have a close connection to the life and career of Ji
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