A penalized likelihood method for multi-group structural equation modelling

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)499-522
Number of pages24
JournalBritish Journal of Mathematical and Statistical Psychology
Volume71
Issue number3
DOIs
Publication statusPublished - 2018 Nov 1

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Penalized Likelihood
Likelihood Methods
Increment
Structural Equation Modeling
Statistical Modeling
Conditional Expectation
Sparsity
Null
Machine Learning
Optimise
Numerical Experiment
Statistics
Decompose
Research
Population
Demonstrate

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Arts and Humanities (miscellaneous)
  • Psychology(all)

Cite this

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A penalized likelihood method for multi-group structural equation modelling. / Huang, Po-Hsien.

In: British Journal of Mathematical and Statistical Psychology, Vol. 71, No. 3, 01.11.2018, p. 499-522.

Research output: Contribution to journalArticle

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