Bayesian Influence and Frequentist Interface

Ming-Hui Chen (), Dipak K. Dey (), Peter Müller (), Dongchu Sun () and Keying Ye ()
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Ming-Hui Chen: University of Connecticut, Department of Statistics
Dipak K. Dey: University of Connecticut, Department of Statistics
Peter Müller: The University of Texas, M. D. Anderson Cancer Center, Department of Biostatistics
Dongchu Sun: University of Missouri-Columbia, Department of Statistics
Keying Ye: University of Texas at San Antonio, Department of Management Science and Statistics, College of Business

Chapter Chapter 7 in Frontiers of Statistical Decision Making and Bayesian Analysis, 2010, pp 219-256 from Springer

Abstract: Abstract Under the Bayesian paradigm to statistical inference the posterior probability distribution contains in principle all relevant information. All statistical inference can be deduced from the posterior distribution by reporting appropriate summaries. This coherent nature of Bayesian inference can give rise to problems when the implied posterior summaries are unduly sensitive to some detail choices of the model. This chapter discusses summaries and diagnostics that highlight such sensitivity and ways to choose a prior probability model to match some desired (frequentist) summaries of the implied posterior inference.

Keywords: Posterior Variance; Small Area Estimation; Perturbation Class; Multivariate Normal Model; Coronary Incident (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4419-6944-6_7

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DOI: 10.1007/978-1-4419-6944-6_7

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