An estimating equations approach for the liscomp model

Beth A. Reboussin, Kung Yee Liang

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)165-182
Number of pages18
Issue number2
StatePublished - Jun 1998
Externally publishedYes


  • Estimating equations
  • LISCOMP model
  • Latent variable
  • Multivariate binary data

ASJC Scopus subject areas

  • Psychology(all)
  • Applied Mathematics


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