On a Sequential Subset Selection Procedure for Exponential Family Distributions

Abstract

The paper deals with the problem of selecting the best population among k populations belonging to the same class of exponential family distributions through sequential subset selection approach. We desire that the best population should be selected and each selected population should be good. Based on the modified likelihood ratio of the conditional frequency function of some statistics, an elimination-type sequential subset selection procedure is proposed. This sequential procedure achieves the selection goal with guaranteed probability at least P* for some prespecified value P*. At each stage, this procedure also provides some statistical inference about an upper bound on the measure of separation from the unknown best population of each remaining contending population. Finally, a modified sequential procedure to select a good population is also studied. This modified sequential procedure achieves the goal of selecting a good population with guaranteed at least P*.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1988
Accession Number
ADA200013

Entities

People

  • Tachen Liang

Organizations

  • Purdue University

Tags

DTIC Thesaurus Topics

  • Classification
  • Computing-Related Activities
  • Data Science
  • Distribution Functions
  • Elimination
  • Frequency
  • Inequalities
  • Information Science
  • Military Research
  • Observation
  • Probability
  • Random Variables
  • Security
  • Statistical Analysis
  • Statistical Inference
  • Statistics
  • Universities

Fields of Study

  • Mathematics

Readers

  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Statistical inference.

Technology Areas

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