Residential College | false |
Status | 已發表Published |
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 Publication | Structural Equation Modeling |
ISSN | 1070-5511 |
Volume | 26Issue: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. |
Keyword | Growth Mixture Modeling K-fold Cross-validation Approach Model Selection |
DOI | 10.1080/10705511.2018.1500140 |
URL | View the original |
Indexed By | SCIE ; SSCI |
Language | 英語English |
WOS Research Area | Mathematics ; Mathematical Methods In Social Sciences |
WOS Subject | Mathematics, Interdisciplinary Applications ; Social Sciences, Mathematical Methods |
WOS ID | WOS:000456517100006 |
Scopus ID | 2-s2.0-85052153360 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | University of Macau |
Corresponding Author | He, Jinbo |
Affiliation | 1.Tianjin University, China 2.University of Macau, Macao 3.Chinese University of Hong Kong (Shenzhen), Hong Kong |
First Author Affilication | University of Macau |
Corresponding Author Affilication | University 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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