2014•Communication in Statistics- Theory and MethodsRequires access

Unbiased Estimator for a Covariance Matrix Under Two-Step Monotone Incomplete Sample

Shin-ichi Tsukada

Open publisher page 8 citations

Abstract

In this article, we consider an inference for a covariance matrix under two-step monotone incomplete sample. The maximum likelihood estimator of the mean vector is unbiased but that of the covariance matrix is biased. We derive an unbiased estimator for the covariance matrix using some fundamental properties of the Wishart matrix. The properties of the estimators are investigated and the accuracies are checked by a numerical simulation.

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What this paper is about

In this article, we consider an inference for a covariance matrix under two-step monotone incomplete sample. The maximum likelihood estimator of the mean vector is unbiased but that of the covariance matrix is biased. We derive an unbiased estimator for the covariance matrix using some fundamental properties of the Wishart matrix. The properties of the estimators are investigated and the accuracies are checked by a numerical simulation.

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Available abstract

In this article, we consider an inference for a covariance matrix under two-step monotone incomplete sample. The maximum likelihood estimator of the mean vector is unbiased but that of the covariance matrix is biased. We derive an unbiased estimator for the covariance matrix using some fundamental properties of the Wishart matrix. The properties of the estimators are investigated and the accuracies are checked by a numerical simulation.

Key concepts: Estimation of covariance matrices, Rational quadratic covariance function, Mathematics, Law of total covariance, Covariance matrix, Scatter matrix, Wishart distribution, Applied mathematics

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