Maximum likelihood estimation of two-level latent variable models with mixed continuous and polytomous data.
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.
Lee
S Y
SY
Department of Statistics, The Chinese University of Hong Kong, Shatin. sylee@sparc2.sta.cuhk.edu.hk
Shi
J Q
JQ
D000163:Acquired Immunodeficiency Syndrome / Q000517:prevention & control / Q000635:transmission; D000465:Algorithms; D001699:Biometry; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D009010:Monte Carlo Method; D012307:Risk Factors; D011477:Sex Work