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- W2063617810 abstract "SUMMARYLet S represent the usual unbiased estimator of a covariance matrix, Σ0, whose elements are functions of a parameter vector . A generalized least squares (G.L.S) estimate, of may be obtained by minimizing where V is some positive definite matrix. Asymptotic properties of the G.L.S. estimators are investigated assuming only that satisfies certain regularity conditions and that the limiting distribution of S is multivariate normal with specified parameters. The estimator of which is obtained by maximizing the Wishart likelihood function (M.W.L. estimator) is shown to be a member of the class of G.L.S. estimators with minimum asymptotic variances. When is linear in a G.L.S. estimator which converges stochastically to the M.W.L. estimator involves far less computation. Methods for calculating estimates of , estimates of the dispersion matrix of , and test statistics, are given for certain linear models." @default.
- W2063617810 created "2016-06-24" @default.
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- W2063617810 date "1973-06-01" @default.
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- W2063617810 title "GENERALIZED LEAST SQUARES ESTIMATORS IN THE ANALYSIS OF COVARIANCE STRUCTURES" @default.
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- W2063617810 doi "https://doi.org/10.1002/j.2333-8504.1973.tb00197.x" @default.
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