AbstractPuntanen et al. [J. Statist. Plann. Inference 88 (2000) 173] provided two matrix-based proofs of the result stating that a linear estimator By represents the best linear unbiased estimator (BLUE) of the expectation vector Xβ under the general Gauss–Markov model M={y,Xβ,σ2V} if and only if B(X:VX⊥)=(X:0), where X⊥ is any matrix whose columns span the orthogonal complement to the column space of X. In this note, still another development of such a characterization is proposed with reference to the BLUE of any vector of estimable parametric functions Kβ. From the algebraic point of view, the present development seems to be the simplest from among all accessible in the literature till now
AbstractGiven matrices A, B and vectors a, b, a necessary and sufficient condition is established fo...
AbstractNecessary and sufficient conditions are derived for the BLUE in a general multiple-partition...
For a general linear regression model we construct a directional statistic which maximizes the proba...
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...
AbstractNew results in matrix algebra applied to the fundamental bordered matrix of linear estimatio...
In this article we consider the partitioned fixed linear model F : y = X1β1 + X2β2 + ε" and the corr...
AbstractSome necessary and sufficient conditions are given for two equalities of ordinary least-squa...
New results in matrix algebra applied to the fundamental bordered matrix of linear estimation theory...
For a general linear regression model we construct a directional statistic which maximizes the proba...
AbstractNew results in matrix algebra applied to the fundamental bordered matrix of linear estimatio...
Let Y be a vector of random variables such that E(Y)=Xβ where β is a vector of unknown parameters an...
Best linear unbiased estimators (BLUE’s) are known to be optimal in many respects under normal assum...
summary:The paper deals with the linear model with uncorrelated observations. The dispersions of the...
summary:The paper deals with the linear model with uncorrelated observations. The dispersions of the...
AbstractGiven matrices A, B and vectors a, b, a necessary and sufficient condition is established fo...
AbstractNecessary and sufficient conditions are derived for the BLUE in a general multiple-partition...
For a general linear regression model we construct a directional statistic which maximizes the proba...
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...
AbstractNew results in matrix algebra applied to the fundamental bordered matrix of linear estimatio...
In this article we consider the partitioned fixed linear model F : y = X1β1 + X2β2 + ε" and the corr...
AbstractSome necessary and sufficient conditions are given for two equalities of ordinary least-squa...
New results in matrix algebra applied to the fundamental bordered matrix of linear estimation theory...
For a general linear regression model we construct a directional statistic which maximizes the proba...
AbstractNew results in matrix algebra applied to the fundamental bordered matrix of linear estimatio...
Let Y be a vector of random variables such that E(Y)=Xβ where β is a vector of unknown parameters an...
Best linear unbiased estimators (BLUE’s) are known to be optimal in many respects under normal assum...
summary:The paper deals with the linear model with uncorrelated observations. The dispersions of the...
summary:The paper deals with the linear model with uncorrelated observations. The dispersions of the...
AbstractGiven matrices A, B and vectors a, b, a necessary and sufficient condition is established fo...
AbstractNecessary and sufficient conditions are derived for the BLUE in a general multiple-partition...
For a general linear regression model we construct a directional statistic which maximizes the proba...