Multiple Decision Rules for Comparing Several Populations with a Fixed Known Standard.

Abstract

Independent observations are available from k univariate distributions indexed by a real parameter theata. It is desired to select that distribution with the largest parameter value unless this value is smaller than some fixed standard theata sub 0 in which case no distribution is to be selected. Various single-stage procedures for this (k+1)-decision problem are discussed, using indifference zone, decision theoretic, Bayesian, and subset selection approaches.

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1975
Accession Number
ADA014119

Entities

People

  • Bruce W. Turnbull

Organizations

  • Cornell University

Tags

DTIC Thesaurus Topics

  • Acquisition
  • Observation
  • Standards

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.

Technology Areas

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