Add to ORKGsummary:Dispersion of measurement results is an important parameter that enables us not only to characterize not only accuracy of measurement but enables us also to construct confidence regions and to test statistical hypotheses. In nonlinear regression model the estimator of dispersion is influenced by a curvature of the manifold of the mean value of the observation vector. The aim of the paper is to find the way how to determine a tolerable level of this curvature
summary:A construction of confidence regions in nonlinear regression models is difficult mainly in t...
summary:In nonlinear regression models with constraints a linearization of the model leads to a bias...
summary:In nonlinear regression models with constraints a linearization of the model leads to a bias...
summary:Dispersion of measurement results is an important parameter that enables us not only to char...
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:In weakly nonlinear regression model a weakly nonlinear hypothesis can be tested by linear m...
summary:Unknown parameters of the covariance matrix (variance components) of the observation vector ...
summary:Unknown parameters of the covariance matrix (variance components) of the observation vector ...
summary:In weakly nonlinear regression model a weakly nonlinear hypothesis can be tested by linear m...
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: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:A construction of confidence regions in nonlinear regression models is difficult mainly in t...
summary:A construction of confidence regions in nonlinear regression models is difficult mainly in t...
summary:In nonlinear regression models with constraints a linearization of the model leads to a bias...
summary:In nonlinear regression models with constraints a linearization of the model leads to a bias...
summary:Dispersion of measurement results is an important parameter that enables us not only to char...
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:In weakly nonlinear regression model a weakly nonlinear hypothesis can be tested by linear m...
summary:Unknown parameters of the covariance matrix (variance components) of the observation vector ...
summary:Unknown parameters of the covariance matrix (variance components) of the observation vector ...
summary:In weakly nonlinear regression model a weakly nonlinear hypothesis can be tested by linear m...
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: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:A construction of confidence regions in nonlinear regression models is difficult mainly in t...
summary:A construction of confidence regions in nonlinear regression models is difficult mainly in t...
summary:In nonlinear regression models with constraints a linearization of the model leads to a bias...
summary:In nonlinear regression models with constraints a linearization of the model leads to a bias...