Copula Structural Equation Models for Mediation Pathway Analysis

Fuente: arXiv
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Autores principales: Chen, Canyi, Kundu, Ritoban, Hao, Wei, Song, Peter X. -K.
Formato: Preprint
Publicado: 2025
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author Chen, Canyi
Kundu, Ritoban
Hao, Wei
Song, Peter X. -K.
author_facet Chen, Canyi
Kundu, Ritoban
Hao, Wei
Song, Peter X. -K.
contents Structural equation models (SEMs) are fundamental to causal mediation pathway discovery. However, traditional SEM approaches often rely on \emph{ad hoc} model specifications when handling complex data structures such as mixed data types or non-normal data in which Gaussian assumptions for errors are rather restrictive. The invocation of copula dependence modeling methods to extend the classical linear SEMs mitigates several of key technical limitations, offering greater modeling flexibility to analyze non-Gaussian data. This paper presents a selective review of major developments in this area, highlighting recent advancements and their methodological implications.
format Preprint
id arxiv_https___arxiv_org_abs_2510_18115
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Copula Structural Equation Models for Mediation Pathway Analysis
Chen, Canyi
Kundu, Ritoban
Hao, Wei
Song, Peter X. -K.
Methodology
Structural equation models (SEMs) are fundamental to causal mediation pathway discovery. However, traditional SEM approaches often rely on \emph{ad hoc} model specifications when handling complex data structures such as mixed data types or non-normal data in which Gaussian assumptions for errors are rather restrictive. The invocation of copula dependence modeling methods to extend the classical linear SEMs mitigates several of key technical limitations, offering greater modeling flexibility to analyze non-Gaussian data. This paper presents a selective review of major developments in this area, highlighting recent advancements and their methodological implications.
title Copula Structural Equation Models for Mediation Pathway Analysis
topic Methodology
url https://arxiv.org/abs/2510.18115