Puntanen, Simo; Styan, George P. H.; Tian, Yongge Three rank formulas associated with the covariance matrices of the BLUE and the OLSE in the general linear model. (English) Zbl 1072.62049 Econom. Theory 21, No. 3, 659-663 (2005). Summary: We consider the estimation of the expectation vector \(X\beta\) under the general linear model \(\{ y,X\beta,\sigma^2V\}\). We introduce a new handy representation for the rank of the difference of the covariance matrices of the ordinary least squares estimator OLSE(\(X\beta\)) (= \(Hy\), say) and the best linear unbiased estimator BLUE(\(X\beta\)) (= \(Gy\), say). From this formula, some well-known conditions for the equality between \(Hy\) and \(Gy\) follow at once. We recall that the equality between \(Hy\) and \(Gy\) can be characterized by the rank-subtractivity ordering between the covariance matrices of \(y\) and \(Hy\). This rank characterization suggests a particular presentation for the rank of the difference of the covariance matrices of \(Hy\) and \(Gy\). We show, however, that this presentation is valid if and only if the model is connected. Cited in 14 Documents MSC: 62H12 Estimation in multivariate analysis 62J05 Linear regression; mixed models PDF BibTeX XML Cite \textit{S. Puntanen} et al., Econom. Theory 21, No. 3, 659--663 (2005; Zbl 1072.62049) Full Text: DOI References: [1] DOI: 10.1137/0117110 · Zbl 0193.47301 · doi:10.1137/0117110 [2] DOI: 10.1016/S0024-3795(01)00297-X · Zbl 0988.15002 · doi:10.1016/S0024-3795(01)00297-X [3] DOI: 10.1016/0024-3795(90)90349-H · Zbl 0695.62152 · doi:10.1016/0024-3795(90)90349-H [4] DOI: 10.1080/03610929608831694 · Zbl 0875.62288 · doi:10.1080/03610929608831694 [5] DOI: 10.1080/03081087408817070 · doi:10.1080/03081087408817070 [6] DOI: 10.2307/2685062 · doi:10.2307/2685062 This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.