On Some Gamma-Minimax Subset Selection and Multiple Comparison Procedures.

Abstract

The use of partial or incomplete prior information in statistical inference has led to the development of the gamma-minimax criterion which allows one to select a decision rule that minimizes the maximum expected risk over gamma. In this paper, the authors are concerned with the problem of selecting a subset containing the 'best' population and containing all 'superior' populations and of multiple comparison procedures which are optimal by using gamma-minimax criterion. Some applications are discussed. Asymptotic optimal nonparametric procedures are also considered. (Author)

Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1974
Accession Number
ADA000253

Entities

People

  • Deng Yuang Huang
  • Shanti Gupta

Organizations

  • Purdue University

Tags

DTIC Thesaurus Topics

  • Computing-Related Activities
  • Data Science
  • Information Science
  • Interdisciplinary Science
  • Mathematical Analysis
  • Mathematics
  • Statistical Inference

Fields of Study

  • Mathematics

Readers

  • International Journalism and Media Studies.
  • Statistical inference.

Technology Areas

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