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Communication Dans Un Congrès Année : 2023

RGCCA for Structural Equation Modeling with Latent and Emergent Variables

Résumé

In this work, we show how to use Regularized Generalized Canonical Correlation Analysis (RGCCA) in structural equation modeling with latent and/or emergent variables. This new approach produces consistent and asymptotically normal estimators of the parameters. RGCCA relies on a well-grounded optimization problem and the global convergence of the algorithm used to solve this problem is guaranteed. We also propose a maximum likelihood (ML) estimation method for estimating the parameters of the model. RGCCA and ML are evaluated in a Monte Carlo simulation and lead to similar results. RGCCA and ML are also compared on the ECSI data for the mobile phone industry and produce very close results.
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Dates et versions

hal-04502942 , version 1 (13-03-2024)

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  • HAL Id : hal-04502942 , version 1

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Arthur Tenenhaus, Michel Tenenhaus, Theo Dijkstra. RGCCA for Structural Equation Modeling with Latent and Emergent Variables. International Conference on Data Science, Nov 2023, Santiago (Chile), Chile. ⟨hal-04502942⟩
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