Maximum likelihood estimators are obtained for multivariate components of variance models under the condition that the effect covariance matrix is positive semidefinite with a maximum rank. The rank of the estimator is random. The estimation procedure leads to a likelihood ratio test that the rank of the effect matrix is not greater than a given number against the alternative that the rank is not greater than a larger specified number. Linear structural relationship models and some factor analytic models can be put into this framework.
Publié le : 1986-06-14
Classification:
Multivariate analysis of variance,
linear structural models,
factor analysis models,
62H12,
62J10
@article{1176349929,
author = {Anderson, Blair M. and Anderson, T. W. and Olkin, Ingram},
title = {Maximum Likelihood Estimators and Likelihood Ratio Criteria in Multivariate Components of Variance},
journal = {Ann. Statist.},
volume = {14},
number = {2},
year = {1986},
pages = { 405-417},
language = {en},
url = {http://dml.mathdoc.fr/item/1176349929}
}
Anderson, Blair M.; Anderson, T. W.; Olkin, Ingram. Maximum Likelihood Estimators and Likelihood Ratio Criteria in Multivariate Components of Variance. Ann. Statist., Tome 14 (1986) no. 2, pp. 405-417. http://gdmltest.u-ga.fr/item/1176349929/