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An Estimating Equations Approach for the LISCOMP Model

Published online by Cambridge University Press:  01 January 2025

Beth A. Reboussin  and
Kung-Yee Liang
Beth A. Reboussin*
Affiliation:
Wake Forest University School of Medicine
Kung-Yee Liang
Affiliation:
Johns Hopkins University, School of Hygiene and Public Health
*
Requests for reprints should be sent to Beth A. Reboussin, Wake Forest University School of Medicine, Department of Public Health Sciences, Medical Center Boulevard, Winston-Salem, NC 27157.
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Abstract

Maximum likelihood estimation is computationally infeasible for latent variable models involving multivariate categorical responses, in particular for the LISCOMP model. A three-stage generalized least squares approach introduced by Muthén (1983, 1984) can experience problems of instability, bias, non-convergence, and non-positive definiteness of weight matrices in situations of low prevalence, small sample size and large numbers of observed indicator variables. We propose a quadratic estimating equations approach that only requires specification of the first two moments. By performing simultaneous estimation of parameters, this method does not encounter the problems mentioned above and experiences gains in efficiency. Methods are compared through a numerical study and an application to a study of life-events and neurotic illness.

Keywords

estimating equationslatent variableLISCOMP modelmultivariate binary data

Information

Type
Original Paper
Information
Psychometrika , Volume 63 , Issue 2 , June 1998 , pp. 165 - 182
Copyright
Copyright © 1998 The Psychometric Society

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Footnotes

The authors would like to thank Bengt Muthén for many helpful discussions and Scott Henderson for generously providing the Canberra data set. This work was supported in part by grant number GM49909 of the National Institutes of Health.

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