Skip to main content

Classification in PLS Path Models and Local Model Optimisation

  • Conference paper
  • 2198 Accesses

Abstract

In this paper, a methodology is proposed which can be used for the identification of classes of units showing homogeneous behavioural models estimated through PLS Path Modelling. The proposed methodology aims at discovering or validating the existence of classes of units in PLS Path models in a predictive-oriented logic, such as it has been proposed, in the framework of PLS Regression, with PLS Typological Regression. An application to a study on customer satisfaction and loyalty is shown.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   159.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • AMATO, S. and BALZANO, S. (2003): Exploratory approaches to group comparison. In: M. Vilares, M. Tenenhaus, P. Coelho, V. Esposito Vinzi and A. Morineau (Eds.): PLS and Related Methods. DECISIA, France, 443–452.

    Google Scholar 

  • ESPOSITO VINZI, V. and LAURO, C. (2003): PLS Regression and Classification. In: M. Vilares, M. Tenenhaus, P. Coelho, V. Esposito Vinzi and A. Morineau (Eds.): PLS and Related Methods. DECISIA, France, 45–56.

    Google Scholar 

  • FORNELL, C. (1992): A national customer satisfaction barometer: the Swedish experience. Journal of Marketing, 56, 6–21.

    Google Scholar 

  • SAS (1999): SAS/STAT ®User’s Guide, Version 8. SAS Institute Inc, Cary, NC.

    Google Scholar 

  • SJÖSTRÖM, M. et al. (1986): PLS Discriminant Plots. In: Proceedings of PARC in Practice. Elsevier, North Holland.

    Google Scholar 

  • TENENHAUS, M. (1998): La Régression PLS: théorie et pratique. Technip, Paris.

    Google Scholar 

  • TENENHAUS, M. et al. (2005): PLS Path Modeling. Computational Statistics and Data Analysis, 48, 159–205.

    Article  MathSciNet  Google Scholar 

  • WOLD, S. et al. (1984): Multivariate Data Analysis in Chemistry. SIAM Journal of Scientific and Statistical Computing, 5, 735–744.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer Berlin · Heidelberg

About this paper

Cite this paper

Squillacciotti, S. (2006). Classification in PLS Path Models and Local Model Optimisation. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_28

Download citation

Publish with us

Policies and ethics