On Finding the Largest Normal Mean and Estimating the Selected Mean

Abstract

For k > or = 2 independent normal populations with unknown means and a common known variance the problem of selecting the population with the largest mean and simultaneously estimating the mean of the selected population is considered in the decision theoretic approach following Cohen and Sackrowitz (1988). Under several loss functions with two additive components due to selection and due to estimation, Bayes decision rules are derived and studied. Both the case of equal sample sizes and the case of unequal sample sizes are treated. The natural rule, which selects in terms of the largest sample mean and then estimates based on the sample mean of the selected population, is critically examined in all situations considered.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1989
Accession Number
ADA211628

Entities

People

  • Klaus J. Miescke
  • Shanti Gupta

Organizations

  • Purdue University

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Air Force
  • Estimators
  • Illinois
  • Mathematical Analysis
  • Military Research
  • Normal Distribution
  • Permutations
  • Precision
  • Probability
  • Random Variables
  • Sampling
  • Scientific Research
  • Sequences
  • Statistical Samples
  • Statistics
  • Theorems
  • Universities

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.

Technology Areas

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