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- W2362546382 abstract "Consider the linear model: Y=Xβ+e, where E(e)=0, Cov=σ~2V, V is a nonnegative definite matrix. It is well known that μ~*=X(X′X)-X′Y and ■=X(X′T-X)-X′T-Y are respectively the least squares and the best linear unbiased estimators of μ=Xβ, where T=V+XUX′, U is a symmetric matrix satisfying rank(T)=rank(V:x) and T≥0. In this paper, a bound similar to Haberman's is obtained when a certain condition is satisfied. If the vector norm involved is taken as the Euclidean one, a set of necessary and suffciant conditions that is easily applicable for Haborman's condition to be true is obtained. We prove an extended form of a bound similar to that of [2], and also extend bound [3] to that V≥0." @default.
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- W2362546382 date "1989-01-01" @default.
- W2362546382 modified "2023-09-26" @default.
- W2362546382 title "A NOTE ON A BOUND OF THE NORM OF THE DIFFERENCE BETWEEN THE LEAST SQUEARS AND THE BEST LINEAR UNBIASED ESTIMATORS OF THE MEAN VECTOR IN A LINEAR MODEL" @default.
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