Clustered Sparse Structural Equation Modeling for Heterogeneous Data

Clustered Sparse Structural Equation Modeling for Heterogeneous Data

Joint analysis with clustering and structural equation modeling is one of the most popular approaches to analyzing heterogeneous data. The methods involved in this approach estimate a path diagram of the same shape for each cluster and interpret the clusters according to the magnitude of the coeffic...

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Bibliographic Details
Published in:Journal of classification Vol. 40; no. 3; pp. 588 - 613
Main Authors: Takasawa, Ippei, Tanioka, Kensuke, Yadohisa, Hiroshi
Format: Journal Article
Language:English
Published: New York Springer US 01-11-2023
Springer Nature B.V
Subjects:
Bioinformatics
Clustering
Data analysis
Estimation
Marketing
Mathematical models
Mathematics and Statistics
Numerical methods
Pattern Recognition
Psychometrics
Signal,Image and Speech Processing
Statistical Theory and Methods
Statistics
Path diagram
SEM
Factor analysis
Clustering
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Summary:Joint analysis with clustering and structural equation modeling is one of the most popular approaches to analyzing heterogeneous data. The methods involved in this approach estimate a path diagram of the same shape for each cluster and interpret the clusters according to the magnitude of the coefficients. However, these methods have problems with difficulty in interpreting the coefficients when the number of clusters and/or paths increases and are unable to deal with any situation where the path diagram for each cluster is different. To tackle these problems, we propose two methods for simplifying the path structure and facilitating interpretation by estimating a different form of path diagram for each cluster using sparse estimation. The proposed methods and related methods are compared using numerical simulation and real data examples. The proposed methods are superior to the existing methods in terms of both fitting and interpretation.
ISSN:0176-4268
1432-1343
DOI:10.1007/s00357-023-09449-9