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.
| 原文 | English |
|---|---|
| 頁（從 - 到） | 499-522 |
| 頁數 | 24 |
| 期刊 | British Journal of Mathematical and Statistical Psychology |
| 卷 | 71 |
| 發行號 | 3 |
| DOIs | |
| 出版狀態 | Published - 2018 十一月 1 |
指紋
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Arts and Humanities (miscellaneous)
- Psychology(all)
引用此文
}
A penalized likelihood method for multi-group structural equation modelling. / Huang, Po-Hsien.
於: British Journal of Mathematical and Statistical Psychology, 卷 71, 編號 3, 01.11.2018, p. 499-522.研究成果: Article
TY - JOUR
T1 - A penalized likelihood method for multi-group structural equation modelling
AU - Huang, Po-Hsien
PY - 2018/11/1
Y1 - 2018/11/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85042788037&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85042788037&partnerID=8YFLogxK
U2 - 10.1111/bmsp.12130
DO - 10.1111/bmsp.12130
M3 - Article
C2 - 29500879
AN - SCOPUS:85042788037
VL - 71
SP - 499
EP - 522
JO - British Journal of Mathematical and Statistical Psychology
JF - British Journal of Mathematical and Statistical Psychology
SN - 0007-1102
IS - 3
ER -