Frontiers of Statistical Decision Making and Bayesian Analysis (Record no. 110709)

000 -LEADER
fixed length control field 04280nam a22004575i 4500
001 - CONTROL NUMBER
control field 978-1-4419-6944-6
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20140220084511.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 100726s2010 xxu| s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781441969446
-- 978-1-4419-6944-6
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-1-4419-6944-6
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA276-280
072 #7 - SUBJECT CATEGORY CODE
Subject category code PBT
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code MAT029000
Source bisacsh
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.5
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Chen, Ming-Hui.
Relator term editor.
245 10 - TITLE STATEMENT
Title Frontiers of Statistical Decision Making and Bayesian Analysis
Medium [electronic resource] :
Remainder of title In Honor of James O. Berger /
Statement of responsibility, etc edited by Ming-Hui Chen, Peter Müller, Dongchu Sun, Keying Ye, Dipak K. Dey.
264 #1 -
-- New York, NY :
-- Springer New York,
-- 2010.
300 ## - PHYSICAL DESCRIPTION
Extent XXIV, 636p.
Other physical details online resource.
336 ## -
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-- txt
-- rdacontent
337 ## -
-- computer
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-- rdamedia
338 ## -
-- online resource
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347 ## -
-- text file
-- PDF
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505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Objective Bayesian Inference with Applications -- Bayesian Decision Based Estimation and Predictive Inference -- Bayesian Model Selection and Hypothesis Tests -- Bayesian Inference for Complex Computer Models -- Bayesian Nonparametrics and Semi-parametrics -- Bayesian Influence and Frequentist Interface -- Bayesian Clinical Trials -- Bayesian Methods for Genomics, Molecular and Systems Biology -- Bayesian Data Mining and Machine Learning -- Bayesian Inference in Political Science, Finance, and Marketing Research -- Bayesian Categorical Data Analysis -- Bayesian Geophysical, Spatial and Temporal Statistics -- Posterior Simulation and Monte Carlo Methods.
520 ## - SUMMARY, ETC.
Summary, etc 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.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Statistics.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematical statistics.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Statistics.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Statistical Theory and Methods.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Müller, Peter.
Relator term editor.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Sun, Dongchu.
Relator term editor.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Ye, Keying.
Relator term editor.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Dey, Dipak K.
Relator term editor.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9781441969439
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-1-4419-6944-6
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