Kariya and Eaton and Kariya studied a robustness property of the usual tests for serial correlation against departure from normality. When the results were applied to a regression model $y = X \beta + u(X: nxk),$ it was assumed that the column space of $X$ is spanned by some $k$ latent vectors of the covariance matrix of error term $u.$ In this paper we delete this assumption and in a much broader class of distributions derive a locally best invariant test for a one-sided problem and a locally best unbiased and invariant test for a two-sided problem. The null distributions of these tests are the same as those under normality.
Publié le : 1980-09-14
Classification:
Robustness,
LBI test,
LBUI test,
serial correlation,
invariance,
Durbin-Watson test,
least squares regression,
62G10,
62F05,
62G35,
62J05
@article{1176345143,
author = {Kariya, Takeaki},
title = {Locally Robust Tests for Serial Correlation in Least Squares Regression},
journal = {Ann. Statist.},
volume = {8},
number = {1},
year = {1980},
pages = { 1065-1070},
language = {en},
url = {http://dml.mathdoc.fr/item/1176345143}
}
Kariya, Takeaki. Locally Robust Tests for Serial Correlation in Least Squares Regression. Ann. Statist., Tome 8 (1980) no. 1, pp. 1065-1070. http://gdmltest.u-ga.fr/item/1176345143/