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    題名: A Penalized Likelihood Method for Multi-Group Structural Equation Modeling
    作者: 黃柏僩
    Huang, Po-Hsien
    貢獻者: 心理系
    日期: 2018-03
    上傳時間: 2021-08-10 16:43:36 (UTC+8)
    摘要: In the past two decades, statistical modelling with sparsity has become an active research topic in the fields of statistics and machine learning. Recently, Huang, Chen and Weng (2017, Psychometrika, 82, 329) and Jacobucci, Grimm, and McArdle (2016, Structural Equation Modeling: A Multidisciplinary Journal, 23, 555) both proposed sparse estimation methods for structural equation modelling (SEM). These methods, however, are restricted to performing single-group analysis. The aim of the present work is to establish a penalized likelihood (PL) method for multi-group SEM. Our proposed method decomposes each group model parameter into a common reference component and a group-specific increment component. By penalizing the increment components, the heterogeneity of parameter values across the population can be explored since the null group-specific effects are expected to diminish. We developed an expectation-conditional maximization algorithm to optimize the PL criteria. A numerical experiment and a real data example are presented to demonstrate the potential utility of the proposed method.
    關聯: British Journal of Mathematical and Statistical Psychology, Vol.71, pp.499-522
    資料類型: article
    DOI 連結: https://doi.org/10.1111/bmsp.12130
    DOI: 10.1111/bmsp.12130
    顯示於類別:[心理學系] 期刊論文

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