On variance-covariance components estimation in linear models with AR(1) disturbances.
Witkovský, V. (1996)
Acta Mathematica Universitatis Comenianae. New Series
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Displaying similar documents to “Variance function estimation via model selection”
On variance-covariance components estimation in linear models with AR(1) disturbances.
Estimates of Relative Risk.
J.M. Begun (1987)
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Estimation of the first order parameters in the twoepoch linear model
Karel Hron (2007)
Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica
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The linear regression model, where the mean value parameters are divided into stable and nonstable part in each of both epochs of measurement, is considered in this paper. Then, equivalent formulas of the best linear unbiased estimators of this parameters in both epochs using partitioned matrix inverse are derived.
Fixed precision estimation of the parameters of a linear regression model with unknown covariance structure
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Modified minimax quadratic estimation of variance components
Viktor Witkovský (1998)
Kybernetika
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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.