Add to ORKGsummary:Unknown parameters of the covariance matrix (variance components) of the observation vector in regression models are an unpleasant obstacle in a construction of the best estimator of the unknown parameters of the mean value of the observation vector. Estimators of variance componets must be utilized and then it is difficult to obtain the distribution of the estimators of the mean value parameters. The situation is more complicated in the case of nonlinearity of the regression model. The aim of the paper is to contribute to a solution of the mentioned problem
summary:Nowadays, the algorithm most frequently used for determination of the estimators of paramete...
summary:Nowadays, the algorithm most frequently used for determination of the estimators of paramete...
summary:Some remarks to problems of point and interval estimation, testing and problems of outliers ...
summary:Unknown parameters of the covariance matrix (variance components) of the observation vector ...
summary:A large number of parameters in regression models can be serious obstacle for processing and...
summary:A large number of parameters in regression models can be serious obstacle for processing and...
summary:Nonsensitiveness regions for estimators of linear functions, for confidence ellipsoids, for ...
summary:In mixed linear statistical models the best linear unbiased estimators need a known covarian...
summary:In mixed linear statistical models the best linear unbiased estimators need a known covarian...
summary:Dispersion of measurement results is an important parameter that enables us not only to char...
summary:Dispersion of measurement results is an important parameter that enables us not only to char...
summary:If an observation vector in a nonlinear regression model is normally distributed, then an al...
summary:If an observation vector in a nonlinear regression model is normally distributed, then an al...
summary:The aim of the paper is to determine an influence of uncertainties in design and covariance ...
summary:The aim of the paper is to determine an influence of uncertainties in design and covariance ...
summary:Nowadays, the algorithm most frequently used for determination of the estimators of paramete...
summary:Nowadays, the algorithm most frequently used for determination of the estimators of paramete...
summary:Some remarks to problems of point and interval estimation, testing and problems of outliers ...
summary:Unknown parameters of the covariance matrix (variance components) of the observation vector ...
summary:A large number of parameters in regression models can be serious obstacle for processing and...
summary:A large number of parameters in regression models can be serious obstacle for processing and...
summary:Nonsensitiveness regions for estimators of linear functions, for confidence ellipsoids, for ...
summary:In mixed linear statistical models the best linear unbiased estimators need a known covarian...
summary:In mixed linear statistical models the best linear unbiased estimators need a known covarian...
summary:Dispersion of measurement results is an important parameter that enables us not only to char...
summary:Dispersion of measurement results is an important parameter that enables us not only to char...
summary:If an observation vector in a nonlinear regression model is normally distributed, then an al...
summary:If an observation vector in a nonlinear regression model is normally distributed, then an al...
summary:The aim of the paper is to determine an influence of uncertainties in design and covariance ...
summary:The aim of the paper is to determine an influence of uncertainties in design and covariance ...
summary:Nowadays, the algorithm most frequently used for determination of the estimators of paramete...
summary:Nowadays, the algorithm most frequently used for determination of the estimators of paramete...
summary:Some remarks to problems of point and interval estimation, testing and problems of outliers ...