Bayesian multivariate latent class profile analysis: exploring the developmental progression of youth depression and substance use

DOI10.1016/j.csda.2021.107261OpenAlexW3159019972MaRDI QIDQ2242033

Saebom Jeon, Jung Wun Lee, Hwan Chung

Publication date: 9 November 2021

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2021.107261

zbMATH Keywords

longitudinal dataMarkov chain Monte Carlolatent class analysislabel switchingsubstance useadolescent depression

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• BayesDA

Cites Work

• Asymptotic Behaviour of the Posterior Distribution in Overfitted Mixture Models
• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Bayesian inferences of latent class models with an unknown number of classes
• Bayesian analysis of stochastic volatility models with fat-tails and correlated errors
• A weakly informative default prior distribution for logistic and other regression models
• Variational approximations in Bayesian model selection for finite mixture distributions
• Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions
• Estimating the dimension of a model
• Dealing with multiple local modalities in latent class profile analysis
• Inference from iterative simulation using multiple sequences
• Estimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniques*
• Latent Variable Regression for Multiple Discrete Outcomes
• Latent Class Model Diagnosis
• Bayesian Measures of Model Complexity and Fit
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