IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v36y2021i1d10.1007_s00180-020-01015-w.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-020-01015-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-020-01015-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rosaria Romano & Francesco Palumbo, 2021. "Partial possibilistic regression path modeling: handling uncertainty in path modeling," Computational Statistics, Springer, vol. 36(1), pages 615-639, March.
    2. Cristina Davino & Pasquale Dolce & Stefania Taralli & Domenico Vistocco, 2022. "Composite-Based Path Modeling for Conditional Quantiles Prediction. An Application to Assess Health Differences at Local Level in a Well-Being Perspective," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 161(2), pages 907-936, June.
    3. Tenenhaus, Arthur & Philippe, Cathy & Frouin, Vincent, 2015. "Kernel Generalized Canonical Correlation Analysis," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 114-131.
    4. Heungsun Hwang & Gyeongcheol Cho, 2020. "Global Least Squares Path Modeling: A Full-Information Alternative to Partial Least Squares Path Modeling," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 947-972, December.
    5. Michel Tenenhaus & Arthur Tenenhaus & Patrick J. F. Groenen, 2017. "Regularized Generalized Canonical Correlation Analysis: A Framework for Sequential Multiblock Component Methods," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 737-777, September.
    6. Arthur Tenenhaus & Michel Tenenhaus, 2011. "Regularized Generalized Canonical Correlation Analysis," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 257-284, April.
    7. Jörg Henseler & Marko Sarstedt, 2013. "Goodness-of-fit indices for partial least squares path modeling," Computational Statistics, Springer, vol. 28(2), pages 565-580, April.
    8. Tenenhaus, Arthur & Tenenhaus, Michel, 2014. "Regularized generalized canonical correlation analysis for multiblock or multigroup data analysis," European Journal of Operational Research, Elsevier, vol. 238(2), pages 391-403.
    9. 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.
    10. Francesca Petrarca & Silvia Terzi, 2018. "The Global Competitiveness Index: an alternative measure with endogenously derived weights," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(5), pages 2197-2219, September.
    11. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:36:y:2021:i:1:d:10.1007_s00180-020-01015-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.