Some equalities and inequalities for covariance matrices of estimators under linear model

Yongge Tian ()

Statistical Papers, 2017, vol. 58, issue 2, No 9, 467-484

Abstract: Abstract Best linear unbiased estimators (BLUEs) of unknown parameters under linear models have minimum covariance matrices in the Löwner partial ordering among all linear unbiased estimators of the unknown parameters. Hence, BLUEs’ covariance matrices are usually used as a criterion to compare optimality with other types of estimator. During this work, people often need to establish certain equalities and inequalities for BLUEs’ covariance matrices, and use them in statistical inference of regression models. This paper aims at establishing some analytical formulas for calculating ranks and inertias of BLUEs’ covariance matrices under general linear model, and using these formulas in the comparison of covariance matrices of BLUEs with other types of estimator. This is in fact a mathematical work, and some new tools in matrix analysis are essentially utilized.

Keywords: General linear model; BLUE; Covariance matrix; Rank; Inertia; Löwner partial ordering; 62H12; 62J05; 62J10 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00362-015-0707-x

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