Skip to main content
SpringerLink
Log in
Book cover

Data Analysis, Machine Learning and Applications pp 201–208Cite as

Non Parametric Control Chart by Multivariate Additive Partial Least Squares via Spline

  • Conference paper

Abstract

Statistical process control (SPC) chart is aimed at monitoring a process over time in order to detect any special event that may occur and find assignable causes for it. Controlling both product quality variables and process variables is a complex problem. Multivariate methods permit to treat all the data simultaneously extracting information on the “directionality” of the process variation. Highlighting the dependence relationships between process variables and product quality variables, we propose the construction of a non-parametric chart, based on Multivariate Additive Partial Least Squares Splines; proper control limits are built by applying the Bootstrap approach.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

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

  • CHAKRABORTI, S., VAN DER LAAN, P. and BAKIR, S.T. (2001): Nonparametric control chart: an overview and some results. Journal of Quality Technology, 33, pp. 304-315.

    Google Scholar 

  • 208Rosaria Lombardo, Amalia Vanacore and Jean-Francçois Durand

    Google Scholar 

  • DURAND, J.F. (2001): Local Polynomial additive regression through PLS and splines: PLSS. Chemiometric and Intelligent Laboratory systems, 58, pp. 235-246.

    Article  Google Scholar 

  • HAWKINS, D.M. (1991): Multivariate Quality Control based on regression-adjusted vari-ables.Technometrics, 33, pp. 61-75.

    Article  Google Scholar 

  • HOTELLING, H. (1947): Multivariate Quality Control, in Techniques of Statistical Analysis. Eds. Eisenhart, Hastay and Wallis, MacGraw Hill, New York.

    Google Scholar 

  • JACKSON, J. E. (1991): A User Guide to Principal Component. Wiley, New York.

    Book  Google Scholar 

  • JONES, A. L. and WOODALL, W.H. (1998): The performance of Bootstrap Cotrol Charts. Journal of Quality Technology, 30, pp. 362-375.

    Google Scholar 

  • KOURTI, T. and MACGREGOR, J. F. (1996): Multivariate SPC Methods for Process and Product Monitoring. Journal of Quality Technology, 28, pp. 409-428.

    Google Scholar 

  • LIU, Y.R. (1995): Control Charts for Multivariate Processes. Journal of the American Statis-tical Association, 90, pp. 1380-1387.

    Article  MATH  Google Scholar 

  • LIU, Y.R. and TANG, J. (1996): Control Charts for Dependent and Independent Measure-ment based on Bootstrap Methods.Journal of the American Statistical Association, 91, pp. 1694-1700.

    Article  MATH  MathSciNet  Google Scholar 

  • LOMBARDO, R., DURAND, J.F., DE VEAUX, R. (2007): Multivariate Additive Partial Least Squares via Splines. Submitted.

    Google Scholar 

  • LOWRY, C. A., WOODALL, W. H., CHAMP, C. W., RIGDON, S. E. (1992): A multivariate EWMA control chart. Technometrics, 34, pp. 46-53.

    Article  MATH  Google Scholar 

  • MACGREGOR, J.F. (1997): Using On-line Process Data to Improve Quality: Challenges for Statisticians.International Statistical Review, 65, pp. 309-323.

    Article  MATH  Google Scholar 

  • NOMIKOS, P. and MACGREGOR, J.F. (1995): Multivariate SPC Charts for Monitoring Batch Processes. Technometrics, 37, pp. 41-59.

    Article  MATH  Google Scholar 

  • TENENHAUS, M. (1998): La Règression PLS, Thèorie et Pratique. Eds. Technip, Paris.

    MATH  Google Scholar 

  • VANACORE, A. and LOMBARDO, R. (2005): Multivariate Statistical Control Charts by non-linear Partial Least Squares.Proceedings of Cladag 2005 conference, pp. 525-528.

    Google Scholar 

  • WOLD, H. (1966): Estimation of principal components and related models by iterative least squares. In Multivariate Analysis, (Eds.) P.R. Krishnaiah, New York: Academic Press, pp. 391-420.

    Google Scholar 

  • WOODALL, W. H. and NCUBE, M. M. (1985): Multivariate CUSUM quality control proce-dures. Technometrics, 27, pp. 285-292.

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Economics, Second University of Naples, Italy

    Rosaria Lombardo

  2. Faculty of Engineering, University of Naples “Federico II”, Italy

    Amalia Vanacore

  3. Faculty of Maths, University of Montpelier II, France

    Jean-FrancÇois Durand

Authors
  1. Rosaria Lombardo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amalia Vanacore
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jean-FrancÇois Durand
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Computer Science and Institute of Business Economics and Information Systems, University of Hildesheim, Marienburgerplatz 22, 31141, Hildesheim, Germany

    Christine Preisach

  2. Lehrstuhl für Mustererkennung und Bildverarbeitung, Universität Freiburg, Gebäude 052, 79110, Freiburg i. Br, Germany

    Hans Burkhardt

  3. Institute of Computer Science and Institute of Business Economics and Information Systems, Marienburgerplatz 22, 31141, Hildesheim, Germany

    Lars Schmidt-Thieme

  4. Fakultät für Wirtschaftswissenschaften, Lehrstuhl für Betriebswirtschaftslehre, insbes. Marketing, Universitätsstraße 25, 33615, Bielefeld, Germany

    Reinhold Decker

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lombardo, R., Vanacore, A., Durand, JF. (2008). Non Parametric Control Chart by Multivariate Additive Partial Least Squares via Spline. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78246-9_24

Download citation

Publish with us

Policies and ethics

Search

Navigation