On the Convergence Rates of Empirical Bayes Rules for Two-Action Problems. Discrete Case.

Abstract

The purpose of this paper is to investigate the convergence rates of a sequence of empirical Bayes decision rules for the two-action decision problems where the distributions of the observations belong to a discrete exponential family. It is found that the sequence of the empirical Bayes decision rules under study is asymptotically optimal, and the order of associated convergence rates is O(exp(-cn)), for some positive constant c, where n is the number of accumulated past experience (observations) at hand. Two examples are provided to illustrate the performance of the proposed empirical Bayes decision rules. A comparison is also made between the proposed empirical Bayes rules and some earlier existng empirical Bayes rules.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1987
Accession Number
ADA181356

Entities

People

  • Ta Chen Liang

Organizations

  • Purdue University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Convergence
  • Data Science
  • Decision Theory
  • Information Science
  • Military Research
  • Observation
  • Probability
  • Probability Density Functions
  • Random Variables
  • Sequences
  • Statistical Decision Theory
  • Statistics
  • Theorems
  • Universities

Fields of Study

  • Mathematics

Readers

  • Computational Modeling and Simulation
  • Mathematical Modeling and Probability Theory.
  • Neural Network Machine Learning.