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Communication Dans Un Congrès Année : 2023

Structural Equation Modeling with Latent/Emergent Variables: RGCCAc

Résumé

We present how to use Regularized Generalized Canonical Correlation Analysis (RGCCA) in structural equation modeling with latent and/or emergent variables. This new approach, named consistent RGCCAc (RGCCAc), produces consistent and asymptotically normal estimators of the parameters. RGCCAc relies on a well-grounded optimization problem and the global convergence of the algorithm used to solve this problem is guaranteed. RGCCAc contains composite models as special case, keeps the robustness and simplicity of PLSc and cSEM and corrects their shortcomings. RGCCAc, cSEM and Maximum Likelhood (ML) based-approach are evaluated in a Monte Carlo simulation and on a case study and produce similar results.
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Dates et versions

hal-04502913 , version 1 (13-03-2024)

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  • HAL Id : hal-04502913 , version 1

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Arthur Tenenhaus, Michel Tenenhaus, Theo Dijkstra. Structural Equation Modeling with Latent/Emergent Variables: RGCCAc. 14th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, Sep 2023, Salerno (Italy), Italy. ⟨hal-04502913⟩
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