Estimation of a covariance matrix with zeros

Sanjay Chaudhuri, Mathias Drton, Thomas S. Richardson

Research output: Contribution to journalArticlepeer-review

85 Scopus citations

Abstract

We consider estimation of the covariance matrix of a multivariate random vector under the constraint that certain covariances are zero. We first present an algorithm, which we call iterative conditional fitting, for computing the maximum likelihood estimate of the constrained covariance matrix, under the assumption of multivariate normality. In contrast to previous approaches, this algorithm has guaranteed convergence properties. Dropping the assumption of multivariate normality, we show how to estimate the covariance matrix in an empirical likelihood approach. These approaches are then compared via simulation and on an example of gene expression.

Original languageEnglish
Pages (from-to)199-216
Number of pages18
JournalBiometrika
Volume94
Issue number1
DOIs
StatePublished - Mar 2007
Externally publishedYes

Keywords

  • Covariance graph
  • Empirical likelihood
  • Graphical model
  • Marginal independence
  • Maximum likelihood estimation
  • Multivariate normal distribution

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