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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
Stable URL: http://www.jstor.org/stable/2240602
Page Count: 3
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
A New Bound for the Euclidean Norm of the Difference Between the Least Squares and the Best Linear Unbiased Estimators
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Abstract

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.

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