PMID: 11550929Sep 12, 2001Paper

Maximum likelihood estimation of two-level latent variable models with mixed continuous and polytomous data

Biometrics
S Y Lee, J Q Shi

Abstract

Two-level data with hierarchical structure and mixed continuous and polytomous data are very common in biomedical research. In this article, we propose a maximum likelihood approach for analyzing a latent variable model with these data. The maximum likelihood estimates are obtained by a Monte Carlo EM algorithm that involves the Gibbs sampler for approximating the E-step and the M-step and the bridge sampling for monitoring the convergence. The approach is illustrated by a two-level data set concerning the development and preliminary findings from an AIDS preventative intervention for Filipina commercial sex workers where the relationship between some latent quantities is investigated.

References

Jan 1, 1992·Statistical Methods in Medical Research·P M Bentler, J A Stein
Nov 1, 1995·The British Journal of Mathematical and Statistical Psychology·S Y LeeP M Bentler
Jun 1, 1984·IEEE Transactions on Pattern Analysis and Machine Intelligence·S Geman, D Geman

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Citations

Jan 18, 2006·Quality of Life Research : an International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation·Sonja Boehmer, Aleksandra Luszczynska
May 27, 2005·Biostatistics·Michael J Daniels, Sharon-Lise T Normand
May 27, 2006·Journal of Women's Health·William C W WongDavina C Ling
May 4, 2007·International Journal of Nursing Studies·Eleanor A HolroydDavina C Ling
Nov 19, 2005·The British Journal of Mathematical and Statistical Psychology·Jens C Eickhoff, Yasuo Amemiya
Sep 3, 2011·Biometrical Journal. Biometrische Zeitschrift·Yan Liu, Andrea S Foulkes
Apr 15, 2009·The British Journal of Mathematical and Statistical Psychology·Wai-Yin Poon, Hai-Bin Wang
Apr 1, 2005·Multivariate Behavioral Research·Xin-Yuan Song, Sik-Yum Lee
Jan 1, 2003·Multivariate Behavioral Research·Xin-Yuan Song, Sik-Yum Lee
Apr 22, 2005·Biostatistics·Xuan LiuJames S Hodges

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