Empirical Bayes Rules for Selecting Good Populations.

Abstract

A problem of selecting populations better than a control is considered. When the populations are uniformly distributed, empirical Bayes rules are derived for a linear loss function for both the known control parameter and the unknown control parameter cases. When the priors are assumed to have bounded supports, empirical Bayes rules for selecting good populations are derived for distributions with truncation parameters (i.e. the form of the pdf is f(x/theta) = Pi(x)ci(theta)I(O, theta)(x)). Monte Carlo studies are carried out which determine the minimum sample sizes needed to make the relative errors less than epsilon for given epsilon-values. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1981
Accession Number
ADA097619

Entities

People

  • Ping Hsiao
  • Shanti Gupta

Organizations

  • Purdue University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Classification
  • Contracts
  • Governments
  • Mathematics
  • Military Research
  • Probability
  • Probability Density Functions
  • Random Variables
  • Security
  • Sequences
  • Statistics
  • Truncation
  • United States
  • United States Government
  • Universities

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms