Statistical Selection Procedures in Multivariate Models,

Abstract

Selection and ranking problems have been studied over the last thirty years, generally under one of two formulations: Bechhofer's indifference zone approach and Gupta's subset selection approach. This paper deals with subset selection. Subset selection procedures in multivariate models are briefly reviewed. These include: (1) Procedures for selecting the best component in a multivariate normal population in terms of the component means as well as the component variances; (2) Procedures for selecting the best from several multivariate normal populations in terms of the Mahalanobis distance, the generalized variance, and the multiple correlation coefficient; (3) Procedures (fixed sample size as well as inverse sampling) for selecting the most (least) probable cell in a multinominal distribution; (4) Procedures for selecting the best from several multinomial populations in terms of the Shannon entropy function; and (5) Procedures for choosing the best subset of the predictor variables in a linear regression model. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1986
Accession Number
ADA176043

Entities

People

  • S. Panchapakesan
  • Shanti Gupta

Organizations

  • Purdue University

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Cell Count
  • Computations
  • Data Science
  • Information Science
  • Knowledge Management
  • Multivariate Analysis
  • New York
  • North Carolina
  • Operations Research
  • Order Statistics
  • Probability
  • Random Variables
  • Regression Analysis
  • Statistical Algorithms
  • Statistics
  • Surveys
  • United States

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Statistical inference.