Please use this identifier to cite or link to this item: https://doi.org/10.1093/biomet/asm007
Title: Estimation of a covariance matrix with zeros
Authors: Chaudhuri, S. 
Drton, M.
Richardson, T.S.
Keywords: Covariance graph
Empirical likelihood
Graphical model
Marginal independence
Maximum likelihood estimation
Multivariate normal distribution
Issue Date: Mar-2007
Citation: Chaudhuri, S., Drton, M., Richardson, T.S. (2007-03). Estimation of a covariance matrix with zeros. Biometrika 94 (1) : 199-216. ScholarBank@NUS Repository. https://doi.org/10.1093/biomet/asm007
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. © 2007 Biometrika Trust.
Source Title: Biometrika
URI: http://scholarbank.nus.edu.sg/handle/10635/105131
ISSN: 00063444
DOI: 10.1093/biomet/asm007
Appears in Collections:Staff Publications

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