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Asymptotic Robustness of Bayesian Decision Rules in Statistical Decision Theory

  • Yurij S. Kharin
Part of the Theory and Decision Library book series (TDLB, volume 1)

Abstract

We consider the statistical classification problems when conditional probability distributions of observations are given with distortions. Robust decision rules are derived for Tukey’s model with contaminating distributions and for the model with additive distortions of observations. The guaranteed risk values for robust and Bayesian decision rules are found and compared by the method of asymptotic expansions. The results are illustrated for the case of Gaussian observations.

Keywords

Asymptotic Expansion Decision Rule Conditional Probability Distribution Statistical Decision Theory Unknown Density 
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

© Academy of Agricultural Sciences of the GDR, Research Centre of Animal Production, Dummerstorf-Rostock, DDR 2551 Dummerstorf. 1984

Authors and Affiliations

  • Yurij S. Kharin
    • 1
  1. 1.Department of Probability and StatisticsByelorussian UniversityMinskUK

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