Estimating the Covariance Matrix: A New Approach

Tatsuya Kubokawa and M. S. Srivastava
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Tatsuya Kubokawa: Faculty of Economics, University of Tokyo
M. S. Srivastava: Department of Statistics, University of Toronto

No CIRJE-F-162, CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo

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 Shorro k and Zidek (1976) and Sinha (1976).

Pages: 22 pages
Date: 2002-07
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:tky:fseres:2002cf162

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