Latent Variable Mixture Modeling

By B. Muthén. 2001

716 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5d30e679ff0cc1516d5862a32530f751b310fc87

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Citations in the corpus

Local solutions in the estimation of growth mixture models. (2006, methodology): “Although this second solution contains a latent class with rapidly increasing heavy drinking that Muthén and Muthén noted as particularly important theoretically, no latent classes show this pattern in the solution with the highest log-likelihood. The substantive implications of the two solutions ar”
The Impact of Misspecifying Class-Specific Residual Variances in Growth Mixture Models (2008, methodology): “Growth mixture modeling (GMM; B. Muthén, 2001; B. Muthén & Shedden, 1999) has emerged as a powerful new technique for addressing developmental research questions.”
A Latent Transition Mixture Model Using the Three-Step Specification (2014, methodology): “Although some recommend including covariates in the mixture model from the beginning (Muthén, 2001), recent simulation studies recommend that the number of classes be decided in a model without the auxiliary variable (NylundGibson & Masyn, 2011).”
Applying Multigroup Confirmatory Factor Models for Continuous Outcomes to Likert Scale Data Complicates Meaningful Group Comparisons (2004, methodology): “For an introduction to this specification of the growth model, the reader is referred to Muthén (2001b).”
Early Identification of Reading Difficulties Using Heterogeneous Developmental Trajectories (2008, methodology): “More recently, a new modeling technique that takes into consideration the effects of heterogeneity in a sample to provide more reliable predictions for later development has been introduced (Muthén, 2000; Muthén et al., 2002; Muthén & Muthén, 2000).”
The Impact of Ignoring a Level of Nesting Structure in Multilevel Growth Mixture Models: A Monte Carlo Study (2010, methodology): “Muthén (2001b, 2002, 2008) and Vermunt and Magidson (2004b, 2004c) present a general latent variable framework, which includes many of the previously mentioned models and some other models with continuous latent variable for the purpose of classifying observed outcomes (e.g., factor analysis for cla”
LATENT GROWTH MIXTURE MODELING: A SIMULATION STUDY (2007, methodology): “latent classes, those classes can be identified and their parameters estimated (Muthén & Shedden, 1999; Muthén, 2001).”
Types of boredom: An experience sampling approach (2014, result): “The conceptual goal of an LPA is to detect heterogeneity in a sample so as to reveal homogenous subsamples of responses that share a similar pattern (in our case, groups of assessments within students; Muthén 2001).”
Overextraction of latent trajectory classes: Much ado about nothing? Reply to Rindskopf (2003), Muthén (2003), and Cudeck and Henly (2003) (2003, methodology): “…2002), best practice for determining the number of components in a growth mixture model, illustrated in the work of Muthén and colleagues (e.g., B. Muthén, 2001a, 2001b; B. Muthén & Muthén, 2000; B. Muthén & Shedden, 1999) consisted of determining the minimum Bayesian information criterion…”
What to do when feeling bored? Students' strategies for coping with boredom. (2010, background): “O. Muthen, 2001).”
Developmental Dynamics of Math Performance From Preschool to Grade 2 (2004, neutral): “Muthén and Muthén (2000) and B.”
Modeling the developmental trajectories of rational number concept(s) (2015, methodology): “Latent variable mixture models provide two main advantages over traditional modeling, such as cluster analysis, for the modeling of a number of social and behavioral characteristics (Muthén, 2001; Nylund, Asparouhov, & Muthén, 2007).”
Using a Bayesian latent growth curve model to identify trajectories of positive affect and negative events following myocardial infarction. (2005, positive): “Previous attempts to link affect and event reporting data over time to who does and does not meet criteria for depression have not been entirely successful and the GGMM approach attempts data reduction in a fashion that is consistent with the clinician's tendency to classify patients into disease ca”