Skip to main content

On the Properties of the Rank Based Multivariate Exponentially Weighted Moving Average Control Charts

  • Conference paper
Data Analysis, Machine Learning and Applications
  • 6009 Accesses

Abstract

The rank based multivariate exponentially weighted moving average (rMEWMA) control chart was proposed by Messaoud et al. (2005). It is a generalization, using the data depth notion, of the nonparametric EWMA control chart for individual observations proposed by Hackl and Ledolter (1992). The authors approximated its asymptotic in-control performance using an integral equation and assuming that a sufficiently large reference sample is available. The actual paper studies the effect of the use of reference samples of limited amount of observations on the in-control and out-of-control performances of the proposed control chart. Furthermore, general recommendations for the required reference sample sizes are given so that the in-control and out-of-control performances of the rMEWMA control chart approach their asymptotic counterparts.

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

Access this chapter

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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • HACKL, P. and LEDOLTER, J. (1992): A New Nonparametric Quality Control Technique. Communications in Statistics-Simulation and Computation 21, 423-443.

    Article  MATH  MathSciNet  Google Scholar 

  • LIU, R. Y., PARELIUS, J. M., and SINGH, K. (1999): Multivariate Analysis by Data Depth: Descriptive Statistics, Graphics and Inference (with discussion). The Annals of Statistics, 27,783-858.

    MATH  MathSciNet  Google Scholar 

  • MESSAOUD, A. (2006): Monitoring Strategies for Chatter Detection in a Drilling Process. PhD Dissertation, Department of Statistics, University of Dortmund.

    Google Scholar 

  • MESSAOUD, A., THEIS, W., WEIHS, C. and HERING, F. (2005): Application and Use of Multivariate Control Charts in a BTA Deep Hole Drilling Process. In: C. Weihs, and W. Gaul (Eds.): Classification- The Ubiquitous Challenge. Springer, Berlin-Heidelberg, 648-655.

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Messaoud, A., Weihs, C. (2008). On the Properties of the Rank Based Multivariate Exponentially Weighted Moving Average Control Charts. 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_54

Download citation

Publish with us

Policies and ethics