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November, 1978 A Bound for the Euclidean Norm of the Difference Between the Least Squares and the Best Linear Unbiased Estimators
J. K. Baksalary, R. Kala
Ann. Statist. 6(6): 1390-1393 (November, 1978). DOI: 10.1214/aos/1176344383

Abstract

Haberman's bound for a norm of the difference between the least squares and the best linear unbiased estimators in a linear model with nonsingular covariance structure is examined in the particular case when a vector norm involved is taken as the Euclidean one. In this frequently occurring case, a new substantially improved bound is developed which, furthermore, is applicable regardless of any additional condition.

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J. K. Baksalary. R. Kala. "A Bound for the Euclidean Norm of the Difference Between the Least Squares and the Best Linear Unbiased Estimators." Ann. Statist. 6 (6) 1390 - 1393, November, 1978. https://doi.org/10.1214/aos/1176344383

Information

Published: November, 1978
First available in Project Euclid: 12 April 2007

zbMATH: 0392.62051
MathSciNet: MR523772
Digital Object Identifier: 10.1214/aos/1176344383

Subjects:
Primary: 62J05
Secondary: 62J10

Keywords: best linear unbiased estimator , Euclidean norm , least squares estimator , linear model , spectral norm

Rights: Copyright © 1978 Institute of Mathematical Statistics

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Vol.6 • No. 6 • November, 1978
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