A robust testing procedure for the equality of covariance matrices

Shagufta Aslam, David M Rocke

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

In classical statistics the likelihood ratio statistic used in testing hypotheses about covariance matrices does not have a closed form distribution, but asymptotically under strong normality assumptions is a function of the χ2-distribution. This distributional approximation totally fails if the normality assumption is not completely met. In this paper we will present multivariate robust testing procedures for the scatter matrix Σ using S-estimates. We modify the classical likelihood ratio test (LRT) into a robust LRT by substituting the robust estimates in the formula in place of classical estimates. A nonlinear formula is also suggested to approximate the degrees of freedom for the approximated Wishart distribution proposed for S-estimates of the shape matrix Σ. We present simulation results to compare the validity and the efficiency of the robust likelihood test to the classical likelihood test.

Original languageEnglish (US)
Pages (from-to)863-874
Number of pages12
JournalComputational Statistics and Data Analysis
Volume49
Issue number3
DOIs
StatePublished - Jun 1 2005

Keywords

  • Likelihood ratio test
  • S-estimate

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Statistics, Probability and Uncertainty
  • Electrical and Electronic Engineering
  • Computational Mathematics
  • Numerical Analysis
  • Statistics and Probability

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