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Kernel method to estimate nonlinear structural equation models

Author

Listed:
  • Ahmed Ouazza

    (Mohamed First University)

  • Noureddine Rhomari

    (Mohamed First University)

  • Zoubir Zarrouk

    (Mohamed First University)

Abstract

The purpose of this paper is to provide a nonparametric kernel method to estimate nonlinear structural equation models involving the functional effects between the latent variables. This approach is based on the combination of Principal Component Analysis (PCA) and kernel smoothing technique. The results obtained from different simulations on both nonlinear and linear structural models show the great performance of this method. Furthermore, an application on real data using a recovery satisfaction model is presented in this paper. From where, we show the adequacy of our method in capturing the non linearity between some latent variables.

Suggested Citation

  • Ahmed Ouazza & Noureddine Rhomari & Zoubir Zarrouk, 2022. "Kernel method to estimate nonlinear structural equation models," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(5), pages 3465-3480, October.
    • Handle: RePEc:spr:qualqt:v:56:y:2022:i:5:d:10.1007_s11135-021-01274-9
    DOI: 10.1007/s11135-021-01274-9
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    References listed on IDEAS

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    1. Harro Walk, 2005. "Strong universal consistency of smooth kernel regression estimates," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 57(4), pages 665-685, December.
    2. Xin-Yuan Song & Zhao-Hua Lu & Jing-Heng Cai & Edward Ip, 2013. "A Bayesian Modeling Approach for Generalized Semiparametric Structural Equation Models," Psychometrika, Springer;The Psychometric Society, vol. 78(4), pages 624-647, October.
    3. Sik-Yum Lee & Hong-Tu Zhu, 2002. "Maximum likelihood estimation of nonlinear structural equation models," Psychometrika, Springer;The Psychometric Society, vol. 67(2), pages 189-210, June.
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