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Bayesian Influence and Frequentist Interface

In: Frontiers of Statistical Decision Making and Bayesian Analysis

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ming-Hui Chen & Dipak K. Dey & Peter Müller & Dongchu Sun & Keying Ye, 2010. "Bayesian Influence and Frequentist Interface," Springer Books, in: Ming-Hui Chen & Peter Müller & Dongchu Sun & Keying Ye & Dipak K. Dey (ed.), Frontiers of Statistical Decision Making and Bayesian Analysis, chapter 0, pages 219-256, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4419-6944-6_7
    DOI: 10.1007/978-1-4419-6944-6_7
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