Estimating the covariance matrix: a new approach

T. Kubokawa and M. S. Srivastava

Journal of Multivariate Analysis, 2003, vol. 86, issue 1, 28-47

Abstract: In this paper, we consider the problem of estimating the covariance matrix and the generalized variance when the observations follow a nonsingular multivariate normal distribution with unknown mean. A new method is presented to obtain a truncated estimator that utilizes the information available in the sample mean matrix and dominates the James-Stein minimax estimator. Several scale equivariant minimax estimators are also given. This method is then applied to obtain new truncated and improved estimators of the generalized variance; it also provides a new proof to the results of Shorrock and Zidek (Ann. Statist. 4 (1976) 629) and Sinha (J. Multivariate Anal. 6 (1976) 617).

Keywords: Covariance; matrix; Generalized; variance; Minimax; estimation; Improvement; Decision; theory; Stein; result; Bartlett's; decomposition (search for similar items in EconPapers)
Date: 2003
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Citations: View citations in EconPapers (5)

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