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政大機構典藏 > 理學院 > 心理學系 > 期刊論文 > Item 140.119/136776

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/136776

Title:	Penalized Least Squares for Structural Equation Modeling with Ordinal Responses
Authors:	黃柏僩 Huang, Po-Hsien
Contributors:	心理系
Date:	2020-08
Issue Date:	2021-08-10 16:45:04 (UTC+8)
Abstract:	Statistical modeling with sparsity has become an active research topic in the fields of statistics and machine learning. Because the true sparsity pattern of a model is generally unknown aforehand, it is often explored by a sparse estimation procedure, like least absolute shrinkage and selection operator (lasso). In this study, a penalized least squares (PLS) method for structural equation modeling (SEM) with ordinal data is developed. PLS describes data generation by an underlying response approach, and uses a least squares (LS) fitting function to construct a penalized estimation criterion. A numerical simulation was used to compare PLS with existing penalized likelihood (PL) in terms of averaged mean square error, absolute bias, and the correctness of the model. Based on these empirical findings, a hybrid PLS was also proposed to improve both PL and PLS. The hybrid PLS first chooses an optimal sparsity pattern by PL, then estimates model parameters by an unpenalized LS under the model selected by PL. We also extended PLS to cases of mixed type data and multi-group analysis. All proposed methods could be realized in the R package lslx.
Relation:	Multivariate Behavioral Research, Vol.57, No.2-3, pp.279-297
Data Type:	article
DOI 連結:	https://doi.org/10.1080/00273171.2020.1820309
DOI:	10.1080/00273171.2020.1820309
Appears in Collections:	[心理學系] 期刊論文

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