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A New Bound for the Euclidean Norm of the Difference Between the Least Squares and the Best Linear Unbiased Estimators
J. K. Baksalary and R. Kala
The Annals of Statistics
Vol. 8, No. 3 (May, 1980), pp. 679-681
Published by: Institute of Mathematical Statistics
Stable URL: http://www.jstor.org/stable/2240602
Page Count: 3
You can always find the topics here!Topics: Matrices, Unbiased estimators, Least squares, Mathematical vectors, Eigenvalues, Estimators, Linear models, Statistical results, General linear model, Mathematics
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A new bound is established for the Euclidean norm of the difference between the least squares estimator and the best linear unbiased estimator of the vector of expectations in the general linear model. The bound is valid regardless of the rank of the dispersion matrix and is expressed in substantially simpler terms than the bounds given earlier by Haberman and by Baksalary and Kala.
The Annals of Statistics © 1980 Institute of Mathematical Statistics