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Author: Jeon, Saebom
Resulting in 2 citations.
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Jeon, Saebom Seo, Tae Seok Anthony, James C. Chung, Hwan |
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Latent Class Analysis for Repeatedly Measured Multiple Latent Class Variables Multivariate Behavioral Research published online (25 November 2020): DOI: 10.1080/00273171.2020.1848515. Also: https://www.tandfonline.com/doi/full/10.1080/00273171.2020.1848515 Cohort(s): NLSY97 Publisher: Taylor & Francis Keyword(s): Alcohol Use; Drug Use; Modeling, Latent Class Analysis/Latent Transition Analysis; Statistical Analysis Research on stage-sequential shifts across multiple latent classes can be challenging in part because it may not be possible to observe the particular stage-sequential pattern of a single latent class variable directly. In addition, one latent class variable may affect or be affected by other latent class variables and the associations among multiple latent class variables are not likely to be directly observed either. To address this difficulty, we propose a multivariate latent class analysis for longitudinal data, joint latent class profile analysis (JLCPA), which provides a principle for the systematic identification of not only associations among multiple discrete latent variables but sequential patterns of those associations. We also propose the recursive formula to the EM algorithm to overcome the computational burden in estimating the model parameters, and our simulation study shows that the proposed algorithm is much faster in computing estimates than the standard EM method. In this work, we apply a JLCPA using data from the National Longitudinal Survey of Youth 1997 in order to investigate the multiple drug-taking behavior of early-onset drinkers from their adolescence, via young adulthood, to adulthood. |
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Bibliography Citation
Jeon, Saebom, Tae Seok Seo, James C. Anthony and Hwan Chung. "Latent Class Analysis for Repeatedly Measured Multiple Latent Class Variables." Multivariate Behavioral Research published online (25 November 2020): DOI: 10.1080/00273171.2020.1848515.
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| 2. |
Lee, Jung Wun Chung, Hwan Jeon, Saebom |
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Bayesian Multivariate Latent Class Profile Analysis: Exploring the Developmental Progression of Youth Depression and Substance Use Computational Statistics and Data Analysis 161 (September 2021): 107261. Also: https://www.sciencedirect.com/science/article/pii/S0167947321000955 Cohort(s): NLSY97 Publisher: Elsevier Keyword(s): Bayesian; Depression (see also CESD); Modeling, Latent Class Analysis/Latent Transition Analysis; Monte Carlo; Statistical Analysis; Substance Use Multivariate latent class profile analysis (MLCPA) is a useful tool for exploring the stage-sequential process of multiple latent class variables, but the inference can be challenging due to the high-dimensional latent structure of the model. In this paper, a Bayesian approach via Markov chain Monte Carlo (MCMC) is proposed for MLCPA as an alternative to the maximum-likelihood (ML) method. Compared to the ML solution, Bayesian estimates are less sensitive to the set of initial values as well as easier to obtain standard errors. We also address issues in MCMC such as label-switching problem with a dynamic data-dependent prior and computational complexity with a recursive formula. Simulation studies revealed the validity and efficiency of the proposed algorithm. An empirical analysis of MLCPA using the National Longitudinal Survey of Youth 97 (NLSY97) identified a small number of representative developmental progressions of adolescent depression and substance use. |
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Bibliography Citation
Lee, Jung Wun, Hwan Chung and Saebom Jeon. "Bayesian Multivariate Latent Class Profile Analysis: Exploring the Developmental Progression of Youth Depression and Substance Use." Computational Statistics and Data Analysis 161 (September 2021): 107261.
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