Extending the Finite Iterative Method for Computing the Covariance Matrix Implied by a Recursive Path Model

  • Zouhair El HadriEmail author
  • Mohamed Hanafi
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 173)


Given q + p variables (q endogenous variables and p exogenous variables) and the covariance matrix among exogenous variables, how to compute the covariance matrix implied by a given recursive path model connecting these q + p variables? The finite iterative method (FIM) was recently introduced by El Hadri and Hanafi (Electron J Appl Stat Anal 8:84–99, 2015) to perform this task but only when all the variables are standardized (and so the covariance matrix is actually a correlation matrix). In this paper, the extension of FIM to the general case of a covariance matrix case is introduced. Moreover, the computational efficiency of FIM and the well-known Jöreskog’s method is discussed and illustrated.


Finite iterative method (FIM) Jöreskog’s method Covariance matrix Correlation matrix Endogenous variable Exogenous variable 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculté des Sciences, Département de MathématiquesUniversité Ibn Tofail, Equipe de Cryptographie et de Traitement de l’InformationKénitraMaroc
  2. 2.Oniris, Unité de Sensométrie et Chimiométrie, Sensometrics and Chemometrics LaboratoryNantesFrance

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