Robust estimation of multivariate covariance components

Amylou Dueck, Sharon Lohr

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

In many settings, such as interlaboratory testing, small area estimation in sample surveys, and heritability studies, investigators are interested in estimating covariance components for multivariate measurements. However, the presence of outliers can seriously distort estimates obtained using standard procedures such as maximum likelihood. We propose a procedure based on M-estimation for robustly estimating multivariate covariance components in the presence of outliers; the procedure applies to balanced and unbalanced data. We present an algorithm for computing the robust estimates and examine the performance of the estimator through a simulation study. The estimator is used to find covariance components and identify outliers in a study of variability of egg length and breadth measurements of American coots.

Original languageEnglish (US)
Pages (from-to)162-169
Number of pages8
JournalBiometrics
Volume61
Issue number1
DOIs
StatePublished - Mar 1 2005

Keywords

  • Hierarchical models
  • M-estimation
  • Random effects model
  • Residual maximum likelihood
  • Variance components

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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