AbstractNew results in matrix algebra applied to the fundamental bordered matrix of linear estimation theory pave the way towards obtaining additional and informative closed-form expressions for the best linear unbiased estimator (BLUE). The results prove significant in several respects. Indeed, more light is shed on the BLUE structure and on the working of the OLS estimator under nonsphericalness in (possibly) singular models
Best linear unbiased estimators (BLUE’s) are known to be optimal in many respects under normal assum...
summary:If is shown that in linear regression models we do not make a great mistake if we substitute...
summary:If is shown that in linear regression models we do not make a great mistake if we substitute...
AbstractNew results in matrix algebra applied to the fundamental bordered matrix of linear estimatio...
New results in matrix algebra applied to the fundamental bordered matrix of linear estimation theory...
AbstractPuntanen et al. [J. Statist. Plann. Inference 88 (2000) 173] provided two matrix-based proof...
AbstractThis note gives some new forms and shortened proofs for results on the linear model with sin...
AbstractIn the linear model {y,Xβ,V} the inefficiency of the ordinary least squares estimator of Xβ ...
AbstractIn the general Gauss-Markoff model (Y, Xβ, σ2V), when V is singular, there exist linear func...
A linear statistic Fy is called linearly sufficient for the estimable parametric function of X*β und...
Let Y be a vector of random variables such that E(Y)=Xβ where β is a vector of unknown parameters an...
We consider a general Gauss-Markoff model (Y, Xβ, σ2V), where E(Y)=Xβ, D(Y)=σ2V. There may be defici...
We consider a general Gauss-Markoff model (Y, Xβ, σ2V), where E(Y)=Xβ, D(Y)=σ2V. There may be defici...
AbstractConsider the linear model {y,Xβ, σ2V}, where X has full column rank and V is positive defini...
AbstractIn a standard linear model, we explore the optimality of the least squares estimator under a...
Best linear unbiased estimators (BLUE’s) are known to be optimal in many respects under normal assum...
summary:If is shown that in linear regression models we do not make a great mistake if we substitute...
summary:If is shown that in linear regression models we do not make a great mistake if we substitute...
AbstractNew results in matrix algebra applied to the fundamental bordered matrix of linear estimatio...
New results in matrix algebra applied to the fundamental bordered matrix of linear estimation theory...
AbstractPuntanen et al. [J. Statist. Plann. Inference 88 (2000) 173] provided two matrix-based proof...
AbstractThis note gives some new forms and shortened proofs for results on the linear model with sin...
AbstractIn the linear model {y,Xβ,V} the inefficiency of the ordinary least squares estimator of Xβ ...
AbstractIn the general Gauss-Markoff model (Y, Xβ, σ2V), when V is singular, there exist linear func...
A linear statistic Fy is called linearly sufficient for the estimable parametric function of X*β und...
Let Y be a vector of random variables such that E(Y)=Xβ where β is a vector of unknown parameters an...
We consider a general Gauss-Markoff model (Y, Xβ, σ2V), where E(Y)=Xβ, D(Y)=σ2V. There may be defici...
We consider a general Gauss-Markoff model (Y, Xβ, σ2V), where E(Y)=Xβ, D(Y)=σ2V. There may be defici...
AbstractConsider the linear model {y,Xβ, σ2V}, where X has full column rank and V is positive defini...
AbstractIn a standard linear model, we explore the optimality of the least squares estimator under a...
Best linear unbiased estimators (BLUE’s) are known to be optimal in many respects under normal assum...
summary:If is shown that in linear regression models we do not make a great mistake if we substitute...
summary:If is shown that in linear regression models we do not make a great mistake if we substitute...