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Modifications of Bayes Procedures

  • Parimal Mukhopadhyay
Part of the Lecture Notes in Statistics book series (LNS, volume 153)

Abstract

This chapter considers different modifications of Bayes procedures and their applications in finite population sampling. Section 4.2 reviews Bayes least squares prediction or Linear Bayes prediction. Section 4.3 addresses restricted Bayes least squares prediction. The problem of Constrained Bayes prediction and Limited Translation Bayes prediction have been considered in the next section. Applications of these procedures in finite population sampling have been illustrated in various stages. Section 4.5 considers the robustness of a Bayesian predictor derived under a working model with respect to a class of alternative models as developed by Bolfarine et al (1987). Robust Bayes estimation of a finite population mean under a class of contaminated priors as advocated by Ghosh and Kim (1993, 1997) has been addressed in the last section.

Keywords

Predictive Distribution Finite Population Good Linear Unbiased Estimator Posterior Variance Good Linear Unbiased Estimator 
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. 2001

Authors and Affiliations

  • Parimal Mukhopadhyay
    • 1
  1. 1.Applied Statistics UnitIndian Statistical InstituteCalcuttaIndia

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