Residual Diagnostics for Growth Mixture Models

By Chen-Pin Wang, C. Hendricks Brown, Karen Bandeen-Roche. 2005

195 citations recorded by the source corpus.

Subfields: Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a211dc4cd69f1b7193c029a5595fdb66f221127c

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Citations in the corpus

General Growth Mixture Analysis with Antecedents and Consequences of Change (2010, unknown): “Then, the ( )Ckβ parameters are estimated using what is referred to as the pseudo-class draw technique (see Bandeen-Roche, Miglioretti, Zeger, & Rathouz, 1997; Muthén & Asparouhov, 2007; Wang et al., 2005).”
Latent Variable Analysis: Growth Mixture Modeling and Related Techniques for Longitudinal Data (2004, background): “The SK procedure needs further investigation but is offered here as an example of the many possibilities of testing a mixture model against data (see also Wang et al., 2002).”
Taking Stock of Developmental Trajectories of Criminal Activity over the Life Course (2008, background): “In short, extensions of the trajectory methodology can be used to make causal inferences about the impact of turning points and interventions on developmental trajectories (Haviland & Nagin, 2005; Wang et al., 2005).”
How Well Does Growth Mixture Modeling Identify Heterogeneous Growth Trajectories? A Simulation Study Examining GMM's Performance Characteristics (2012, methodology): “Regarding the first theme, Bauer and Curran countered the arguments of Muthen (2003) and Rindskopf (2003) by demonstrating that when data were generated from a homogenous ( k = 1) nonnormal model (a) the LMR LRT consistently rejects a k = 1 in favor of a k = 2, (b) the SK test supported a ”
An introduction to latent variable mixture modeling (part 1): overview and cross-sectional latent class and latent profile analyses. (2014, methodology): “…regression (to predict classes) and equality tests of means across latent classes (to assess mean differences) can be computed based on pseudo-class draws, thereby providing less biased estimates by retaining the “latent” nature of the classes (Asparouhov & Muthén, 2013; Wang et al., 2005).”
Latent Class Analysis With Distal Outcomes: A Flexible Model-Based Approach (2013, methodology): “The second approach, multiple pseudoclass draws (Bandeen-Roche et al., 1997; Wang et al., 2005), mimics the maximum-probability assignment approach, but accounts for the uncertainty in class membership.”
Latent Class Analysis: An Alternative Perspective on Subgroup Analysis in Prevention and Treatment (2013, methodology): “Although several different classify-analyze approaches have been proposed, including single or multiple pseudoclass draws based on posterior probabilities (Wang et al. 2005) and multiple imputation of the latent class variable using a Bayesian approach (Lanza et al.”
Statistical and substantive checking in growth mixture modeling: comment on Bauer and Curran (2003). (2003, methodology): “It should be emphasized that there are many possibilities for checking model fit against data in mixture settings and methodology for this is likely to expand considerably in the future (see, e.g., the residual diagnostic approaches proposed in Wang & Brown, 2002).”
Types of boredom: An experience sampling approach (2014, methodology): “To account for such differences, an Mplus feature allowing for mean comparisons on the basis of pseudo-class draws was employed (Wang et al. 2005).”