Maximum likelihood estimation of two-level latent variable models with mixed continuous and polytomous data |
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Authors: | Lee S Y Shi J Q |
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Affiliation: | Department of Statistics, The Chinese University of Hong Kong, Shatin. sylee@sparc2.sta.cuhk.edu.hk |
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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. |
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Keywords: | Factor analysis Mixed continuous and polytomous data Monte Carlo EM algorithm Two-level latent variable models |
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