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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 ..."
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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.
Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes
 Psychological Methods
, 2003
"... Growth mixture models are often used to determine if subgroups exist within the population that follow qualitatively distinct developmental trajectories. However, statistical theory developed for finite normal mixture models suggests that latent trajectory classes can be estimated even in the absenc ..."
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Cited by 87 (8 self)
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Growth mixture models are often used to determine if subgroups exist within the population that follow qualitatively distinct developmental trajectories. However, statistical theory developed for finite normal mixture models suggests that latent trajectory classes can be estimated even in the absence of population heterogeneity if the distribution of the repeated measures is nonnormal. By drawing on this theory, this article demonstrates that multiple trajectory classes can be estimated and appear optimal for nonnormal data even when only 1 group exists in the population. Further, the withinclass parameter estimates obtained from these models are largely uninterpretable. Significant predictive relationships may be obscured or spurious relationships identified. The implications of these results for applied research are highlighted, and future directions for quantitative developments are suggested. Over the last decade, random coefficient growth modeling has become a centerpiece of longitudinal data analysis. These models have been adopted enthusiastically by applied psychological researchers in part because they provide a more dynamic analysis of repeated measures data than do many traditional techniques. However, these methods are not ideally suited for testing theories that posit the existence of qualitatively different developmental pathways, that is, theories in which distinct developmental pathways are thought to hold within subpopulations. One widely cited theory of this type is Moffitt’s (1993) distinction between “lifecourse persistent ” and “adolescentlimited ” antisocial behavior trajectories. Moffitt’s theory is prototypical of other developmental taxonomies that have been proposed in such diverse areas as developmental psychopathology (Schulenberg,
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 ..."
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Cited by 4 (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...
Selective Mixture of Gaussians Clustering for Location
"... One of the challenges of location fingerprinting to be deployed in the real offices is the training database handling process, which does not scale well with increasing amount of tracking space to be covered. However, little attention was paid to tackle such issue, where the majority of previous wo ..."
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One of the challenges of location fingerprinting to be deployed in the real offices is the training database handling process, which does not scale well with increasing amount of tracking space to be covered. However, little attention was paid to tackle such issue, where the majority of previous work rather focused on improving the tracking accuracy. In this paper, we propose a novel idea to enhance fingerprinting’s processing speed and positioning accuracy with mixture of Gaussians clustering. We realised the key difference between fingerprinting and other unsupervised problems, that is we do know the label (the Cartesian coordinate) of the signal data in advance. This key information was largely ignored in previous work, where the fingerprinting clustering was based solely on the signal data information. By exploiting this information, we tackle the indoor signal multipath and shadowing with twolevel signal data clustering and Cartesian coordinate clustering. We tested our approach in a real office environment with harsh indoor condition, and concluded that our clustering scheme does not only reduce the fingerprinting processing time, but also improves the positioning accuracy.
IMPROVED STATISTICS ESTIMATION AND FEATURE EXTRACTION FOR HYPERSPECTRAL DATA
, 2001
"... This document has been made available through Purdue ePubs, a service of the Purdue University Libraries. Please contact epubs@purdue.edu for additional information. Kuo, BorChen and Landgrebe, David, "IMPROVED STATISTICS ESTIMATION AND FEATURE EXTRACTION FOR ..."
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This document has been made available through Purdue ePubs, a service of the Purdue University Libraries. Please contact epubs@purdue.edu for additional information. Kuo, BorChen and Landgrebe, David, "IMPROVED STATISTICS ESTIMATION AND FEATURE EXTRACTION FOR