Screening among Multivariate Normal Data

Pinyuen Chen, William L. Melvin, Michael C. Wicks

Research output: Contribution to journalArticle

98 Scopus citations

Abstract

This paper considers the problem of screeningkmultivariate normal populations (secondary data) with respect to a control population (primary data) in terms of covariance structure. A screening procedure, developed based upon statistical ranking and selection theory, is designed to include in the selected subset those populations which have the same (or similar) covariance structure as the control population, and exclude those populations which differ significantly. Formulas for computing the probability of a correct selection and the least favorable configuration are developed. The sample size required to achieve a specific probability requirement is also developed, with results presented in tabular form. This secondary data selection procedure is illustrated via an example with applications to radar signal processing.

Original languageEnglish (US)
Pages (from-to)10-29
Number of pages20
JournalJournal of Multivariate Analysis
Volume69
Issue number1
DOIs
StatePublished - Apr 1999

Keywords

  • Eigenvalue
  • Hypergeometric function in matrix argument
  • Indifference zone approach
  • Least favorable configuration
  • Multivariate normal
  • Probability of a correct screening
  • Radar signal processing
  • Ranking and selection
  • Subset selection approach

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

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