Principal Component Analysis Under Correlated Multivariate Regression Equations Model.

Abstract

The motivation behind the study in this paper is to derive some asymptotic results useful in the area of principal component analysis under the CMRE model. The object of the principal component analysis is to select a small number of important linear combinations of the variables which will best describe the variation among experimental units. In this paper, the authors consider the problem of testing for the equality of the last few eigenvalues of the covariance matrix under correlated multivariate regression equations models. Asymptotic distributions of various test statistics are derived when the underlying distribution is multivariate normal. Some of the distribution theory is extended to the case when the underlying distribution is elliptically symmetric.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1985
Accession Number
ADA160266

Entities

People

  • Paruchuri R. Krishnaiah
  • Suman Sarkar

Organizations

  • University of Pittsburgh

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Air Force
  • Analytic Functions
  • Covariance
  • Data Science
  • Distribution Functions
  • Distribution Theory
  • Eigenvalues
  • Equations
  • Factor Analysis
  • Governments
  • Information Science
  • Multivariate Analysis
  • Normal Distribution
  • Power Series
  • Statistical Analysis
  • Statistics
  • United States Government

Fields of Study

  • Mathematics

Readers

  • Linear Algebra
  • Statistical inference.