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Nonparametric covariance estimation in multivariate distributions

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Abstract.

The estimation problem of the unknown covariance matrix of a multivariate distribution with the known mean is studied under a matrix-valued quadratic loss function. The conditions on the sample sizes for the best unbiased estimator to have a smaller risk than the sample covariance matrix is established. The former estimator is completely (without exceptional sets of Lebesgue measure zero) characterized by its expectation in the class of all multivariate distributions with zero mean and finite fourth moments.

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Authors and Affiliations

  1. University of Münster, Institute for Mathematical Statistics, Einsteinstr. 62, D-48149 Münster, Germany, , , , , , DE

    Detlef Plachky

  2. Department of Mathematics & Statistics, University of Maryland at Baltimore County, Baltimore, MD 21250, USA (e-mail: rukhin@math.umbc.edu), , , , , , US

    Andrew L. Rukhin

Authors
  1. Detlef Plachky
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  2. Andrew L. Rukhin
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Received: November 1998

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Plachky, D., Rukhin, A. Nonparametric covariance estimation in multivariate distributions. Metrika 50, 131–136 (1999). https://doi.org/10.1007/s001840050040

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  • DOI: https://doi.org/10.1007/s001840050040

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