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Generalized properties for Hanafi–Wold’s procedure in partial least squares path modeling

Author

Listed:
  • Mohamed Hanafi

    (Oniris)

  • Pasquale Dolce

    (University of Naples Federico II)

  • Zouhair El Hadri

    (Mohammed V University)

Abstract

Partial least squares path modeling is a statistical method that allows to analyze complex dependence relationships among several blocks of observed variables, each one represented by a latent variable. The computation of latent variable scores is an essential step of the method, achieved through an iterative procedure named here Hanafi–Wold’s procedure. The present paper generalizes properties already known in the literature for this procedure, from which additional convergence results will be obtained.

Suggested Citation

  • Mohamed Hanafi & Pasquale Dolce & Zouhair El Hadri, 2021. "Generalized properties for Hanafi–Wold’s procedure in partial least squares path modeling," Computational Statistics, Springer, vol. 36(1), pages 603-614, March.
  • Handle: RePEc:spr:compst:v:36:y:2021:i:1:d:10.1007_s00180-020-01015-w
    DOI: 10.1007/s00180-020-01015-w
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    References listed on IDEAS

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    1. Mohamed Hanafi, 2007. "PLS Path modelling: computation of latent variables with the estimation mode B," Computational Statistics, Springer, vol. 22(2), pages 275-292, July.
    2. Pasquale Dolce & Vincenzo Esposito Vinzi & Natale Carlo Lauro, 2018. "Non-symmetrical composite-based path modeling," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 759-784, September.
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    Cited by:

    1. Mohamed Hanafi & Zouhair El Hadri & Abderrahim Sahli & Pasquale Dolce, 2022. "Overcoming convergence problems in PLS path modelling," Computational Statistics, Springer, vol. 37(5), pages 2437-2470, November.

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