summary: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
summary:In the paper four types of estimations of the linear function of the variance components are...
Variance components estimation originated with estimating error variance in analysis of variance by ...
summary:In the paper four types of estimations of the linear function of the variance components are...
summary:The paper deals with modified minimax quadratic estimation of variance and covariance compon...
summary:The paper deals with modified minimax quadratic estimation of variance and covariance compon...
AbstractThe variance of a quadratic function of the random variables in a linear model is minimized ...
AbstractThe paper consists of two parts. The first part deals with solutions to some optimization pr...
AbstractEstimation of variance components in linear model theory is presented as an application of e...
We write a linear model in the form Y=Xβ+Uξ, where β is an unknown parameter and ξ is a hypothetical...
The paper consists of two parts. The first part deals with solutions to some optimization problems. ...
Multivariate linear models with ellipsoidal restrictions are introduced for the modelling of semipar...
Starting with the general linear model Y=Xβ+ε where E(εε')=θ1V1+ ... +θpVp, the theory of minimum no...
The paper consists of two parts. The first part deals with solutions to some optimization problems. ...
summary:The paper deals with a linear model with linear variance-covariance structure, where the lin...
summary:The paper deals with a linear model with linear variance-covariance structure, where the lin...
summary:In the paper four types of estimations of the linear function of the variance components are...
Variance components estimation originated with estimating error variance in analysis of variance by ...
summary:In the paper four types of estimations of the linear function of the variance components are...
summary:The paper deals with modified minimax quadratic estimation of variance and covariance compon...
summary:The paper deals with modified minimax quadratic estimation of variance and covariance compon...
AbstractThe variance of a quadratic function of the random variables in a linear model is minimized ...
AbstractThe paper consists of two parts. The first part deals with solutions to some optimization pr...
AbstractEstimation of variance components in linear model theory is presented as an application of e...
We write a linear model in the form Y=Xβ+Uξ, where β is an unknown parameter and ξ is a hypothetical...
The paper consists of two parts. The first part deals with solutions to some optimization problems. ...
Multivariate linear models with ellipsoidal restrictions are introduced for the modelling of semipar...
Starting with the general linear model Y=Xβ+ε where E(εε')=θ1V1+ ... +θpVp, the theory of minimum no...
The paper consists of two parts. The first part deals with solutions to some optimization problems. ...
summary:The paper deals with a linear model with linear variance-covariance structure, where the lin...
summary:The paper deals with a linear model with linear variance-covariance structure, where the lin...
summary:In the paper four types of estimations of the linear function of the variance components are...
Variance components estimation originated with estimating error variance in analysis of variance by ...
summary:In the paper four types of estimations of the linear function of the variance components are...