Lecture Notes in Statistics - Springer Verlag - Vol. 152
ROBUST BAYESIAN ANALYSIS
David Rios Insua and Fabrizio Ruggeri Eds.
September 2000. 435 pp. 18 figs. Softcover $59.95
ISBN 0-387-98866-1
Robust Bayesian analysis aims at checking the impact of the inputs (the prior, the model and the loss) to a Bayesian analysis and stems from the difficulty of assessing such inputs in practice. This volume is the first comprehensive overview of the main topics in Bayesian robustness, which has emerged and matured as a fundamental area within Bayesian Statistics.
After a non-technical review, various chapters deal with issues in the foundations of robust Bayesian analysis, local and global robustness, model robustness, loss robustness, and comparisons with other approaches, including model selection and nonparametrics. The volume ends up with a collection of papers on applications and case studies in medicine, reliability and engineering, showing the applicability of robust Bayesian methods. A complete bibliography is also included. The volume will give both researchers and practitioners an opportunity to become quickly and thoroughly acquainted with this field.
David Rios Insua is Professor of Statistics and Head of Department at Universidad Rey Juan Carlos. He is the author of more than fifty papers and seven books and has received research awards from various national and international institutions.
Fabrizio Ruggeri is Senior Researcher at Consiglio Nazionale delle Ricerche and teaches at Politecnico di Milano and Università di Pavia. He is the author of more than fifty papers and one book, one of the organizers of the two International Workshops on Bayesian Robustness, and the Editor of the ISBA (International Society for Bayesian Analysis) Bulletin.
TABLE OF CONTENTS
Introduction
- James O. Berger, David Rios Insua and Fabrizio Ruggeri
Bayesian Robustness
Foundations
- David Rios Insua and Regino Criado
Topics on the Foundations of Robust Bayesian Analysis
Global and Local Robustness
- Elias Moreno
Global Bayesian Robustness for some Classes of Priors
- Paul Gustafson
Local Robustness in Bayesian Analysis
- Siva Sivaganesan
Global and Local Robustness Approaches: Uses and Limitations
- Sandra Fortini and Fabrizio Ruggeri
On the Use of the Concentration Function in Bayesian Robustness
Likelihood robustness
- N.D. Shyamalkumar
Likelihood Robustness
Loss robustness
- Dipak Dey and Micheas Athanasios
Ranges of Posterior Expected Losses and epsilon--Robust Actions
- Jacinto Martin and J. Pablo Arias
Computing the Efficient Set in Bayesian Decision Problems
- Joseph Kadane, Gabriella Salinetti and Cidambi Srinivasan
Stability of Bayes Decisions and Applications
Comparison with other statistical methods
- Brunero Liseo
Robustness Issues in Bayesian Model Selection
- Sanjib Basu
Bayesian Robustness and Bayesian Nonparametrics
- Brani Vidakovic
Gamma Minimax: a Paradigm for Conservative Robust Bayesians
Algorithms
- Michael Lavine, Marco Perone Pacifico, Gabriella Salinetti and Luca Tardella
Linearization Techniques in Bayesian Robustness
- Bruno Betrò and Alessandra Guglielmi
Methods for Global Prior Robustness under Generalized Moment Conditions
- Steven MacEachern and Peter Müller
Efficient MCMC Schemes for
Robust Model Extensions using Encompassing Dirichlet Process
Mixture Models
Case studies
- Concha Bielza, Sixto Rios Insua, M. Gomez and J.A.
Fernandez del Pozo
Sensitivity Analysis in IctNeo
- Enrico Cagno, Franco Caron, Mauro Mancini and Fabrizio Ruggeri
Sensitivity of Replacement Priorities for Gas Pipeline Maintenance
- Bradley P. Carlin and Maria Eglee Perez
Robust Bayesian Analysis in Medical and Epidemiological Settings
- Juan Miguel Marin
A Robust Version of the Dynamic Linear Model with an Economic
Application
- Michael P. Wiper and Simon Wilson
Prior Robustness in some Common Types of Software Reliability Model
Bibliography on Bayesian Robustness