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Evaluating the Performance of the K-fold Cross-Validation Approach for Model Selection in Growth Mixture Modeling
He, Jinbo1,2; Fan, Xitao3
2019-01-02
Source PublicationStructural Equation Modeling
ISSN1070-5511
Volume26Issue:1Pages:66-79
Abstract

Deciding on the number of “classes” has been the most prominent and most debated challenge in finite mixture modeling. Recently, a novel strategy has been proposed to select the best model in finite mixture modeling: a k-fold cross-validation approach. However, this approach has not been systematically evaluated, which makes the performance of the k-fold cross-validation approach for model selection in finite mixture modeling largely unknown. Thus, the main motivation for conducting the current work is to systematically evaluate the performance of the k-fold cross-validation approach for model selection in the context of Growth Mixture Modeling. Results revealed that the performance of the k-fold cross-validation approach for model selection in GMM is generally unsatisfactory, and it only performs reasonably well under the condition of very large class separation.

KeywordGrowth Mixture Modeling K-fold Cross-validation Approach Model Selection
DOI10.1080/10705511.2018.1500140
URLView the original
Indexed BySCIE ; SSCI
Language英語English
WOS Research AreaMathematics ; Mathematical Methods In Social Sciences
WOS SubjectMathematics, Interdisciplinary Applications ; Social Sciences, Mathematical Methods
WOS IDWOS:000456517100006
Scopus ID2-s2.0-85052153360
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Citation statistics
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorHe, Jinbo
Affiliation1.Tianjin University, China
2.University of Macau, Macao
3.Chinese University of Hong Kong (Shenzhen), Hong Kong
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
He, Jinbo,Fan, Xitao. Evaluating the Performance of the K-fold Cross-Validation Approach for Model Selection in Growth Mixture Modeling[J]. Structural Equation Modeling, 2019, 26(1), 66-79.
APA He, Jinbo., & Fan, Xitao (2019). Evaluating the Performance of the K-fold Cross-Validation Approach for Model Selection in Growth Mixture Modeling. Structural Equation Modeling, 26(1), 66-79.
MLA He, Jinbo,et al."Evaluating the Performance of the K-fold Cross-Validation Approach for Model Selection in Growth Mixture Modeling".Structural Equation Modeling 26.1(2019):66-79.
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