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.
Generalized properties for Hanafi–Wold’s procedure in partial least squares path modeling / Hanafi, M.; Dolce, P.; El Hadri, Z.. - In: COMPUTATIONAL STATISTICS. - ISSN 0943-4062. - 36:1(2021), pp. 603-614. [10.1007/s00180-020-01015-w]
Generalized properties for Hanafi–Wold’s procedure in partial least squares path modeling
Dolce P.
;
2021
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
