Estimation of variance and covariance components--MINQUE theory
AbstractThe paper consists of two parts. The first part deals with solutions to some optimization problems. The general problem is one of minimssing Tr AVA'U, where V and U are positive definite matrices when the elements of the matrix are subject to linear restrictions of the type AX = O or X'AX = O and trace AVi = pi, i = 1,..., k, or U1'AU1 + ... + Uk'AUk = M. These results are used in determining Minimum Norm Quadratic Unbiased Estimators (MINQUE) of variance and covariance components in linear models. The present paper is a generalization of an earlier attempt by the author to obtain estimators of heteroscedastic variances in a regression model. The method is quite general, applicable to all experimental situations, and the computations are simple.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Multivariate Analysis.
Volume (Year): 1 (1971)
Issue (Month): 3 (September)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Wu, Jixiang & Wu, Dongfeng & Jenkins, Johnie N. & McCarty, Jack Jr., 2006. "A recursive approach to detect multivariable conditional variance components and conditional random effects," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 285-300, January.
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