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Frontiers of Statistical Decision Making and Bayesian Analysis By Ming-Hui Chen

Frontiers of Statistical Decision Making and Bayesian Analysis by Ming-Hui Chen

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Summary

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 will be specially printed for your order, which means it may take a little longer to arrive (5 days). We do this to cut down on waste & to help protect our planet.

Frontiers of Statistical Decision Making and Bayesian Analysis Summary

Frontiers of Statistical Decision Making and Bayesian Analysis: In Honor of James O. Berger by Ming-Hui Chen

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.

Frontiers of Statistical Decision Making and Bayesian Analysis 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)

About Ming-Hui Chen

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 Muller 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.

Table of Contents

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.

Additional information

NLS9781489992017
9781489992017
1489992014
Frontiers of Statistical Decision Making and Bayesian Analysis: In Honor of James O. Berger by Ming-Hui Chen
New
Paperback
Springer-Verlag New York Inc.
2014-10-20
631
N/A
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