Latent profile analysis with nonnormal mixtures: a Monte Carlo examination of model selection using fit indices

DOI10.1016/j.csda.2015.02.019zbMath1468.62143OpenAlexW2042613222MaRDI QIDQ1660200

Aaron R. Baggett, Grant B. Morgan, Kari J. Hodge

Publication date: 15 August 2018

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

Full work available at URL: https://doi.org/10.1016/j.csda.2015.02.019

zbMATH Keywords

model selectionmixture modelnonnormal data

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (3)

Editorial: The third special issue on advances in mixture models ⋮ The effect of model misspecification on growth mixture model class enumeration ⋮ Model fit criteria curve behaviour in class enumeration – a diagnostic tool for model (mis)specification in longitudinal mixture modelling

Uses Software

• R
• MULTIMIX
• Mplus
• MplusAutomation

Cites Work

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• A note on 'Testing the number of components in a normal mixture'
• Latent Markov Models for Longitudinal Data
• Regression and time series model selection in small samples
• Model-Based Gaussian and Non-Gaussian Clustering

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