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Matching Priors for Prediction

  • Gauri Sankar Datta
  • Rahul Mukerjee
Part of the Lecture Notes in Statistics book series (LNS, volume 178)

Abstract

In the preceding chapters, we considered probability matching priors for estimation. The object of interest was a parameter, either one-dimensional or multidimensional, and priors ensuring approximate frequentist validity of the associated posterior credible regions were studied. Evidently, the solutions for these matching priors depend on the specification of the interest parameter. For instance, in Example 2.5.7 concerning the Student’s t-model, it was seen that a unique second order matching prior exists when the location parameter θ1 is of interest whereas no such prior is available when interest lies in the shape parameter θ2.

Keywords

Auxiliary Variable Prediction Interval Future Observation Predictive Density Bayesian Prediction 
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-Verlag New York, Inc. 2004

Authors and Affiliations

  • Gauri Sankar Datta
    • 1
  • Rahul Mukerjee
    • 2
  1. 1.Department of StatisticsUniversity of GeorgiaAthensUSA
  2. 2.Indian Institute of ManagementCalcuttaIndia

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