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Author: Cai, Jing-Heng
Resulting in 1 citation.
1. Cai, Jing-Heng
Song, Xin-Yuan
Lam, Kwok-Hap
Ip, Edward Hak-Sing
A Mixture of Generalized Latent Variable Models for Mixed Mode and Heterogeneous Data
Computational Statistics and Data Analysis 55,11 (November 2011): 2889-2907.
Also: http://www.sciencedirect.com/science/article/pii/S0167947311001770
Cohort(s): Children of the NLSY79, NLSY79
Publisher: Elsevier
Keyword(s): Alcohol Use; Bayesian; Behavior Problems Index (BPI); Child Self-Administered Supplement (CSAS); Modeling; Modeling, Growth Curve/Latent Trajectory Analysis; Modeling, Mixed Effects; Monte Carlo; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading)

In the behavioral, biomedical, and social-psychological sciences, mixed data types such as continuous, ordinal, count, and nominal are common. Subpopulations also often exist and contribute to heterogeneity in the data. In this paper, we propose a mixture of generalized latent variable models (GLVMs) to handle mixed types of heterogeneous data. Different link functions are specified to model data of multiple types. A Bayesian approach, together with the Markov chain Monte Carlo (MCMC) method, is used to conduct the analysis. A modified DIC is used for model selection of mixture components in the GLVMs. A simulation study shows that our proposed methodology performs satisfactorily. An application of mixture GLVM to a data set from the National Longitudinal Surveys of Youth (NLSY) is presented. [Copyright Elsevier]

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Bibliography Citation
Cai, Jing-Heng, Xin-Yuan Song, Kwok-Hap Lam and Edward Hak-Sing Ip. "A Mixture of Generalized Latent Variable Models for Mixed Mode and Heterogeneous Data." Computational Statistics and Data Analysis 55,11 (November 2011): 2889-2907.