Matrix rank and inertia formulas in the analysis of general linear models

Yongge Tian

Displaying similar documents to “Matrix rank and inertia formulas in the analysis of general linear models”

On the equality of the ordinary least squares estimators and the best linear unbiased estimators in multivariate growth-curve models.

Gabriela Beganu (2007)

RACSAM

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It is well known that there were proved several necessary and sufficient conditions for the ordinary least squares estimators (OLSE) to be the best linear unbiased estimators (BLUE) of the fixed effects in general linear models. The purpose of this article is to verify one of these conditions given by Zyskind [39, 40]: there exists a matrix Q such that ΩX = XQ, where X and Ω are the design matrix and the covariance matrix, respectively. It will be shown the accessibility of this condition...

Eliminating transformations for nuisance parameters in linear model

Pavla Kunderová (2003)

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica

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Robustness of the best linear unbiased estimator and predictor in linear regression models

František Štulajter (1990)

Aplikace matematiky

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If is shown that in linear regression models we do not make a great mistake if we substitute some sufficiently precise approximations for the unknown covariance matrix and covariance vector in the expressions for computation of the best linear unbiased estimator and predictor.

Variance components and an additional experiment

Lubomír Kubáček (2012)

Applications of Mathematics

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Estimators of parameters of an investigated object can be considered after some time as insufficiently precise. Therefore, an additional measurement must be realized. A model of a measurement, taking into account both the original results and the new ones, has a litle more complicated covariance matrix, since the variance components occur in it. How to deal with them is the aim of the paper.