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Estimation of covariance matrices in fixed and mixed effects linear models

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  • Kubokawa, Tatsuya
  • Tsai, Ming-Tien
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    Abstract

    The estimation of the covariance matrix or the multivariate components of variance is considered in the multivariate linear regression models with effects being fixed or random. In this paper, we propose a new method to show that usual unbiased estimators are improved on by the truncated estimators. The method is based on the Stein-Haff identity, namely the integration by parts in the Wishart distribution, and it allows us to handle the general types of scale-equivariant estimators as well as the general fixed or mixed effects linear models.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 97 (2006)
    Issue (Month): 10 (November)
    Pages: 2242-2261

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    Handle: RePEc:eee:jmvana:v:97:y:2006:i:10:p:2242-2261

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    Related research

    Keywords: Covariance matrix Decision theory Estimation Haff identity Improvement James-Stein estimator Linear regression model Minimaxity Mixed effects model Multivariate normal distribution Stein identity Variance component Wishart distribution;

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