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Bayesian Influence and Frequentist Interface

  • Ming-Hui Chen
  • Dipak K. Dey
  • Peter Müller
  • Dongchu Sun
  • Keying Ye
Chapter

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 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer New York 2010

Authors and Affiliations

  • Ming-Hui Chen
    • 1
  • Dipak K. Dey
    • 1
  • Peter Müller
    • 2
  • Dongchu Sun
    • 3
  • Keying Ye
    • 4
  1. 1.Department of StatisticsUniversity of ConnecticutStorrsUSA
  2. 2.Department of BiostatisticsThe University of Texas, M. D. Anderson Cancer CenterHoustonUSA
  3. 3.Department of StatisticsUniversity of Missouri-ColumbiaColumbiaUSA
  4. 4.Department of Management Science and Statistics, College of BusinessUniversity of Texas at San AntonioSan AntonioUSA

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