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Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling 14
, 2007
"... Mixture modeling is a widely applied data analysis technique used to identify unobserved heterogeneity in a population. Despite mixture models ’ usefulness in practice, one unresolved issue in the application of mixture models is that there is not one commonly accepted statistical indicator for deci ..."
Abstract

Cited by 28 (4 self)
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Mixture modeling is a widely applied data analysis technique used to identify unobserved heterogeneity in a population. Despite mixture models ’ usefulness in practice, one unresolved issue in the application of mixture models is that there is not one commonly accepted statistical indicator for deciding on the number of classes in a study population. This article presents the results of a simulation study that examines the performance of likelihoodbased tests and the traditionally used Information Criterion (ICs) used for determining the number of classes in mixture modeling. We look at the performance of these tests and indexes for 3 types of mixture models: latent class analysis (LCA), a factor mixture model (FMA), and a growth mixture models (GMM). We evaluate the ability of the tests and indexes to correctly identify the number of classes at three different sample sizes (n D 200, 500, 1,000). Whereas the Bayesian Information Criterion performed the best of the ICs, the bootstrap likelihood ratio test proved to be a very consistent indicator of classes across all of the models considered.
Improved Statistics Estimation And Feature Extraction For Hyperspectral Data Classification
, 2001
"... vii CHAPTER 1: ..."
On a Resampling Approach to Choosing the Number of Components in Normal Mixture Models
 Proceedings of Interface 96, 28th Symposium on the Interface
, 1997
"... We consider the fitting of a gcomponent normal mixture to multivariate data. The problem is to test whether g is equal to some specified value versus some specified alternative value. This problem would arise, for example, in the context of a cluster analysis effected by a normal mixture model, wh ..."
Abstract

Cited by 3 (1 self)
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We consider the fitting of a gcomponent normal mixture to multivariate data. The problem is to test whether g is equal to some specified value versus some specified alternative value. This problem would arise, for example, in the context of a cluster analysis effected by a normal mixture model, where the decision on the number of clusters is undertaken by testing for the smallest value of g compatible with the data. A test statistic can be formed in terms of the likelihood ratio. Unfortunately, regularity conditions do not hold for the likelihood ratio statistic to have its usual asymptotic null distribution of chisquared. One approach to the assessment of P values with the use of this statistic is to adopt a resampling approach. An investigation is undertaken of the accuracy of P values assessed in this manner. 1 Introduction Often an important consideration in cluster analysis is deciding on the number of clusters in the data. With a mixture modelbased method of clustering, th...