Exploring Some Analytical Characteristics of Finite Mixture Models

By Robert W. Brame, Dan Nagin, Larry A. Wasserman. 2006

79 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/de890f8f61a59e2144e025c93547cc991afb4c06

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Citations in the corpus

Observations on the Use of Growth Mixture Models in Psychological Research (2007, background): “Bauer & Shanahan (2007) made much the same interpretation of the mixing components within a latent profile analysis, but they also remarked that relatively little research has yet been conducted to see how well the properties of the global distribution are recovered via this local approximation (see”
Is creativity domain-specific? Latent class models of creative accomplishments and creative self-descriptions. (2009, unknown): “The problem of choosing the correct number of classes is a busy area of research (see Bauer & Curran, 2003; Brame, Nagin, & Wasserman, 2006; Nylund, Asparouhov, & Muthe´n, 2007).”
Detecting Specialization in Offending: Comparing Analytic Approaches (2009, methodology): “The model assessment process draws on values for the Bayesian Information Criterion (BIC), which weighs the degree of correspondence between the observed data and the specified model ( Brame et al. 2006; Nylund et al. 2007), as well as the Lo‐Mendell‐Rubin (LMR) test, which compares the model speci”
Are the sources of interest the same for everyone? Using multilevel mixture models to explore individual differences in appraisal structures (2009, methodology): “To date, promising indicators available in Mplus include a likelihood ratio test (LRT), an adjusted LRT, a bootstrapped version of the LRT, and the Bayesian Information Criterion (see Brame, Nagin, & Wasserman, 2006 ; Lo, Mendell, & Rubin, 2000; McLachlan & Peel, 2000 ; Nylund, Asparouhov, & Muthe”
A review of recent approaches for capturing heterogeneity in partial least squares path modelling (2008, background): “, 2001; Andrews and Currim, 2003b; Sarstedt and Schwaiger, 2007, 2008), mixture logit models (Andrews and Currim, 2003a; Brame et al., 2006) or growth mixture models (Nylund et al.”
An Examination of First and Second Generation Immigrant Offending Trajectories (2014, background): “In addition, BIC scores have been found to be less accurate in identifying the correct number of groups when assessing trajectories using smaller samples (Brame et al., 2006).”