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Generalized least squares and maximum likelihood estimations of multivariate polychoric correlations

Generalized least squares and maximum likelihood estimations of multivariate polychoric correlations This paper investigates the generalized least squares estimation and the maximum likelihood estimation of the parameters in a multivariate polychoric correlations model, based on data from a multidimensional contingency table. Asymptotic properties of the estimators are discussed. An iterative procedure based on the Gauss-Newton algorithm is implemented to produce the generalized least squares estimates and the standard errors estimates. It is shown that via an iteratively reweighted method, the algorithm produces the maximum likelihood estimates as well. Numerical results on the finite sample behaviors of the methods are reported. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Acta Mathematicae Applicatae Sinica Springer Journals

Generalized least squares and maximum likelihood estimations of multivariate polychoric correlations

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Publisher
Springer Journals
Copyright
Copyright © 1987 by Science Press, Beijing, China and Allerton Press, Inc. New York, U.S.A.
Subject
Mathematics; Applications of Mathematics; Math Applications in Computer Science; Theoretical, Mathematical and Computational Physics
ISSN
0168-9673
eISSN
1618-3932
DOI
10.1007/BF02008373
Publisher site
See Article on Publisher Site

Abstract

This paper investigates the generalized least squares estimation and the maximum likelihood estimation of the parameters in a multivariate polychoric correlations model, based on data from a multidimensional contingency table. Asymptotic properties of the estimators are discussed. An iterative procedure based on the Gauss-Newton algorithm is implemented to produce the generalized least squares estimates and the standard errors estimates. It is shown that via an iteratively reweighted method, the algorithm produces the maximum likelihood estimates as well. Numerical results on the finite sample behaviors of the methods are reported.

Journal

Acta Mathematicae Applicatae SinicaSpringer Journals

Published: Jul 13, 2005

References