A new relative efficiency in linear model in reference is instructed into the linear weighted regression, and its upper and lower bound are proposed. In the linear weighted regression model, for the best linear unbiased estimation of mean matrix respect to the least-squares estimation, two new relative efficiencies are given, and their upper and lower bounds are also studied
We compare the asymptotic covariance matrix of the ML estimator in a nonlinear measurement error mod...
The Ordinary Least Squares Estimator is an unbiased estimator in estimating parameters in a linear r...
AbstractWe derive simpler expressions under a certain structure of design matrices for the two-stage...
In statistics parameter theory, usually the parameter estimations have two kinds, one is the least-s...
This paper considers the estimation of the coefficient vector in a linear regression model subject t...
We study nonlinear least-squares problem that can be transformed to linear problem by change of vari...
Choosing the performance criterion to be mean squared error matrix, we have compared the least squar...
We first establish two matrix determinant Kantorovich-type inequalities. Then, based on these two an...
A new relative efficiency is defined as LSE and BLUE in the generalized linear model. The relative e...
Asymptotic efficiency, blended weight Hellinger distance, kernel density estimator, linear regressio...
The Ordinary Least Squares Estimator is an unbiased estimator in estimating parameters in a linear r...
Previous results have indicated that the OLS estimator of the vector of regression coefficients can ...
A proof is given of an inequality for the minimum efficiency of least squares estimation in regressi...
AbstractThis paper investigates the efficiencies of several generalized least squares estimators (GL...
We derive simpler expressions under a certain structure of design matrices for the two-stage Aitken ...
We compare the asymptotic covariance matrix of the ML estimator in a nonlinear measurement error mod...
The Ordinary Least Squares Estimator is an unbiased estimator in estimating parameters in a linear r...
AbstractWe derive simpler expressions under a certain structure of design matrices for the two-stage...
In statistics parameter theory, usually the parameter estimations have two kinds, one is the least-s...
This paper considers the estimation of the coefficient vector in a linear regression model subject t...
We study nonlinear least-squares problem that can be transformed to linear problem by change of vari...
Choosing the performance criterion to be mean squared error matrix, we have compared the least squar...
We first establish two matrix determinant Kantorovich-type inequalities. Then, based on these two an...
A new relative efficiency is defined as LSE and BLUE in the generalized linear model. The relative e...
Asymptotic efficiency, blended weight Hellinger distance, kernel density estimator, linear regressio...
The Ordinary Least Squares Estimator is an unbiased estimator in estimating parameters in a linear r...
Previous results have indicated that the OLS estimator of the vector of regression coefficients can ...
A proof is given of an inequality for the minimum efficiency of least squares estimation in regressi...
AbstractThis paper investigates the efficiencies of several generalized least squares estimators (GL...
We derive simpler expressions under a certain structure of design matrices for the two-stage Aitken ...
We compare the asymptotic covariance matrix of the ML estimator in a nonlinear measurement error mod...
The Ordinary Least Squares Estimator is an unbiased estimator in estimating parameters in a linear r...
AbstractWe derive simpler expressions under a certain structure of design matrices for the two-stage...