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PLS Path modelling: computation of latent variables with the estimation mode B

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Abstract

PLS Path modelling has several interesting advantages compared to other existing approaches traditionally used for structural modelling. However, the lack of convergence properties of the existing iterative procedures for the computation of the latent variables, has always been considered as a major drawback. The convergence is stated only in practice. The present paper shows that when the estimation mode B is chosen for all blocks, the iterative procedure for the computation of latent variables proposed by Wold (in Encyclopaedia of statistical sciences, vol 6. Wiley, New York, pp. 581–591, 1985) is monotonically convergent.

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References

  • Bookstein FL (1982). Soft the geometric meaning of soft modeling, with some generalizations. In: Jöreskog KG, Wold H (eds) System under indirect observation, vol 2. North Holland, Amsterdam, pp 55–74

    Google Scholar 

  • Chin WW (2001). PLS GRAPH user’s guide. CT. Bauer Collegue of business, University of Houston, Houston

    Google Scholar 

  • Joreskog KG (1970). A general method for the analysis of covariance structure. Biometrika 57: 239–251

    MathSciNet  Google Scholar 

  • Lohmöller JB (1984) LVPLS programm manuel, Version 1.6. Zentralarchiv für Empirische Sozialforschung, Universität zu Köln, Köln

  • Lohmöller JB (1989). Latent variables path modeling with partial least squares. Physica-Verlag, Heildelberg

    Google Scholar 

  • Mathes H (1993). Global optimisation criteria of the PLS-algorithm in recursive path models with latent variables. In: Haagen K, Bartholomew DJ, Deister M (eds) Statistical modelling and latent variables. Elsevier, Amsterdam

    Google Scholar 

  • Ringle C, Wende S, Will A (2005) SmartPLS. University of Hamburg, Hamburg (http://www. smartpls.de)

  • Tenenhaus M, Esposito Vinzi V, Chatelin YM, Lauro C (2005). PLS path modeling. Comput Stat Data Anal 48:159–205

    Article  MathSciNet  Google Scholar 

  • Wold H (1982). Soft modelling: the basic design and some extensions. In: Jöreskog KG, Wold H (eds) System under indirect observation, vol 2. North Holland, Amsterdam, pp 1–54

    Google Scholar 

  • Wold H (1985). Partial least squares. In: Kotz S, Johnson NL (eds) Encyclopaedia of statistical sciences, vol 6. Wiley, New York, pp 581–591

    Google Scholar 

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  1. ENITIAA-INRA, Unité de Sensometrie et Chimiometrie, Rue de la Géraudière, BP 82225, 44322, Nantes Cedex, France

    Mohamed Hanafi

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  1. Mohamed Hanafi
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Correspondence to Mohamed Hanafi.

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Hanafi, M. PLS Path modelling: computation of latent variables with the estimation mode B. Computational Statistics 22, 275–292 (2007). https://doi.org/10.1007/s00180-007-0042-3

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  • DOI: https://doi.org/10.1007/s00180-007-0042-3

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