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

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...

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Bibliographic Details
Published in:Computational statistics 2021-03, Vol.36 (1), p.603-614
Main Authors: Hanafi, Mohamed, Dolce, Pasquale, El Hadri, Zouhair
Format: Article
Language:English
Subjects:
Economic Theory/Quantitative Economics/Mathematical Methods
Iterative methods
Least squares
Mathematics and Statistics
Original Paper
Probability and Statistics in Computer Science
Probability Theory and Stochastic Processes
Statistics
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Summary: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.
ISSN:0943-4062
1613-9658
DOI:10.1007/s00180-020-01015-w