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June, 1986 Maximum Likelihood Estimators and Likelihood Ratio Criteria in Multivariate Components of Variance
Blair M. Anderson, T. W. Anderson, Ingram Olkin
Ann. Statist. 14(2): 405-417 (June, 1986). DOI: 10.1214/aos/1176349929

Abstract

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.

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Blair M. Anderson. T. W. Anderson. Ingram Olkin. "Maximum Likelihood Estimators and Likelihood Ratio Criteria in Multivariate Components of Variance." Ann. Statist. 14 (2) 405 - 417, June, 1986. https://doi.org/10.1214/aos/1176349929

Information

Published: June, 1986
First available in Project Euclid: 12 April 2007

zbMATH: 0631.62063
MathSciNet: MR840505
Digital Object Identifier: 10.1214/aos/1176349929

Subjects:
Primary: 62H12
Secondary: 62J10

Rights: Copyright © 1986 Institute of Mathematical Statistics

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Vol.14 • No. 2 • June, 1986
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