On variance-covariance components estimation in linear models with AR(1) disturbances.

Witkovský, V.

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Modified minimax quadratic estimation of variance components

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The paper deals with modified minimax quadratic estimation of variance and covariance components under full ellipsoidal restrictions. Based on the, so called, linear approach to estimation variance components, i. e. considering useful local transformation of the original model, we can directly adopt the results from the linear theory. Under normality assumption we can can derive the explicit form of the estimator which is formally find to be the Kuks–Olman type estimator.

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Covariance matrix elements estimation: special linear model without and with repeated measurement

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Estimation of variance components in mixed linear models

Júlia Volaufová, Viktor Witkovský (1992)

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The MINQUE of the linear function $\int^{'} ϑ$ of the unknown variance-components parameter $ϑ$ in mixed linear model under linear restrictions of the type $𝐑 ϑ = c$ is defined and derived. As an illustration of this estimator the example of the one-way classification model with the restrictions $ϑ_{1} = k ϑ_{2}$ , where $k \geq 0$ , is given.

A sufficient condition for admissibility in linear estimation

Czesław Stępniak (1988)

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It was recently shown that all estimators which are locally best in the relative interior of the parameter set, together with their limits constitute a complete class in linear estimation, both unbiased and biased. However, not all these limits are admissible. A sufficient condition for admissibility of a limit was given by the author (1986) for the case of unbiased estimation in a linear model with the natural parameter space. This paper extends this result to the general linear model...