Probability matching priors for predicting a dependent variable with application to regression models

  • Gauri Sankar Datta
  • Rahul Mukerjee
Bayesian Approach

Abstract

In a Bayesian setup, we consider the problem of predicting a dependent variable given an independent variable and past observations on the two variables. An asymptotic formula for the relevant posterior predictive density is worked out. Considering posterior quantiles and highest predictive density regions, we then characterize priors that ensure approximate frequentist validity of Bayesian prediction in the above setting. Application to regression models is also discussed.

Key words and phrases

Bayesian prediction frequentist validity highest predictive density region noninformative prior posterior quantile regression shrinkage argument 

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Copyright information

© The Institute of Statistical Mathematics 2003

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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