Lecture Notes in Statistics - Springer Verlag - Vol. 152


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.




Global and Local Robustness

Likelihood robustness

Loss robustness

Comparison with other statistical methods


Case studies

Bibliography on Bayesian Robustness