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.
Publié le : 1978-11-14
Classification:
Linear model,
least squares estimator,
best linear unbiased estimator,
Euclidean norm,
spectral norm,
62J05,
62J10
@article{1176344383,
author = {Baksalary, J. K. and Kala, R.},
title = {A Bound for the Euclidean Norm of the Difference Between the Least Squares and the Best Linear Unbiased Estimators},
journal = {Ann. Statist.},
volume = {6},
number = {1},
year = {1978},
pages = { 1390-1393},
language = {en},
url = {http://dml.mathdoc.fr/item/1176344383}
}
Baksalary, J. K.; Kala, R. A Bound for the Euclidean Norm of the Difference Between the Least Squares and the Best Linear Unbiased Estimators. Ann. Statist., Tome 6 (1978) no. 1, pp. 1390-1393. http://gdmltest.u-ga.fr/item/1176344383/