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Author: Chung, Hwan
Resulting in 5 citations.
1. Chang, Hsiu-Ching
Chung, Hwan
Dealing with Multiple Local Modalities in Latent Class Profile Analysis
Computational Statistics and Data Analysis 68 (December 2013): 296-310.
Also: http://www.sciencedirect.com/science/article/pii/S0167947313002612
Cohort(s): NLSY97
Publisher: Elsevier
Keyword(s): Alcohol Use; Modeling, Latent Class Analysis/Latent Transition Analysis; Monte Carlo

Parameters for latent class profile analysis (LCPA) are easily estimated by maximum likelihood via the EM algorithm or Bayesian method via Markov chain Monte Carlo. However, the local maximum problem is a long-standing issue in any hill-climbing optimization technique for the LCPA model. To deal with multiple local modalities, two probabilistic optimization techniques using the deterministic annealing framework are proposed. The deterministic annealing approaches are implemented with an efficient recursive formula in the step for the parameter update. The proposed methods are applied to the data from the National Longitudinal Survey of Youth 1997 (NLSY97), a survey that explores the transition from school to work and from adolescence to adulthood in the United States.
Bibliography Citation
Chang, Hsiu-Ching and Hwan Chung. "Dealing with Multiple Local Modalities in Latent Class Profile Analysis." Computational Statistics and Data Analysis 68 (December 2013): 296-310.
2. Chung, Hwan
Anthony, James C.
A Bayesian Approach to a Multiple-Group Latent Class-Profile Analysis: The Timing of Drinking Onset and Subsequent Drinking Behaviors Among U.S. Adolescents
Structural Equation Modeling: A Multidisciplinary Journal 20,4 (2013): 658-680.
Also: http://www.tandfonline.com/doi/full/10.1080/10705511.2013.824783#.UugFYxBOlpg
Cohort(s): NLSY97
Publisher: Lawrence Erlbaum Associates ==> Taylor & Francis
Keyword(s): Adolescent Behavior; Alcohol Use; Bayesian; Modeling, Latent Class Analysis/Latent Transition Analysis; Monte Carlo

Permission to reprint the abstract has been denied by the publisher.

Bibliography Citation
Chung, Hwan and James C. Anthony. "A Bayesian Approach to a Multiple-Group Latent Class-Profile Analysis: The Timing of Drinking Onset and Subsequent Drinking Behaviors Among U.S. Adolescents." Structural Equation Modeling: A Multidisciplinary Journal 20,4 (2013): 658-680.
3. Chung, Hwan
Anthony, James C.
Schafer, Joseph L.
Latent Class Profile Analysis: An Application to Stage Sequential Processes in Early Onset Drinking Behaviours
Journal of the Royal Statistical Society: Series A (Statistics in Society) 174,3 (July 2011): 689–712.
Also: http://onlinelibrary.wiley.com/doi/10.1111/j.1467-985X.2010.00674.x/full
Cohort(s): NLSY97
Publisher: Wiley Online
Keyword(s): Adolescent Behavior; Alcohol Use

Permission to reprint the abstract has not been received from the publisher.

Summary: In longitudinal research on early onset drinkers, much attention has been paid to the identification of subgroups of individuals who follow similar sequential patterns of drinking behaviours. However, research on the sequential development of drinking behaviour can be challenging in part because it may not be possible to observe the particular drinking behaviour stage at a given point in time directly. To address this difficulty, we can use a latent class analysis, which provides a set of principles for the systematic identification of homogeneous subgroups of individuals. In this work, we apply a latent class analysis in an investigation of stage sequential patterns of drinking behaviours among early onset drinkers, using data from the National Longitudinal Survey of Youth 1997. A latent class analysis approach is used to sort different patterns of drinking behaviours into a small number of classes at each measurement occasion; and the class sequencing of early onset drinkers over the entire set of time points is evaluated to identify two or more homogeneous early onset drinkers who exhibit a similar sequence of class memberships over time. This approach uncovers four common drinking behaviours in early onset drinkers over three measurements from early to late adolescence. The sequences of drinking behaviours can be grouped into three sequential patterns representing the most probable progression of early onset drinking behaviours.
Bibliography Citation
Chung, Hwan, James C. Anthony and Joseph L. Schafer. "Latent Class Profile Analysis: An Application to Stage Sequential Processes in Early Onset Drinking Behaviours." Journal of the Royal Statistical Society: Series A (Statistics in Society) 174,3 (July 2011): 689–712. A.
4. Jeon, Saebom
Seo, Tae Seok
Anthony, James C.
Chung, Hwan
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.
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.
5. Lee, Jung Wun
Chung, Hwan
Jeon, Saebom
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.
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.