Skip to main content
SpringerLink
Log in
Book cover

Frontiers in Statistical Quality Control 10 pp 197–206Cite as

A Robust Detection Procedure for Multiple Change Points of Linear Trends

  • Chapter
  • First Online:

  • 1615 Accesses

Part of the book series: Frontiers in Statistical Quality Control ((FSQC,volume 10))

Abstract

A flexible manufacturing system (FMS) enables the production of multiple-items with short production run. By using an automatic measurement system, it is possible to observe a large amount of items in a short time. The observations from the FMS include some variation patterns and outliers, thereby making it difficult to implement a conventional statistical process control. In this study, a retrospective analysis of such a process dataset is proposed. Our procedure detects multiple change points for a dataset with outliers and variation patterns such as shifts and trends. The locally weighted scatter plot smoothing and the jump/roof/valley detection procedure based on a local polynomial kernel smoothing are useful to develop our procedure. We modify these procedures and propose a robust procedure for detecting multiple change points.

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   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

References

  1. Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association,74, 829–836.

    Google Scholar 

  2. Harnish, P., Nelson, B., & Runger, G. (2009). Process partitions from time-ordered clusters. Journal of Quality Technology,41, 3–17.

    Google Scholar 

  3. Joo, J.-H., & Qiu, P. (2009). Jump detection in a regression curve and its derivative. Technometrics,51, 289–305.

    Google Scholar 

  4. Lenth, R. V. (1989). Quick and easy analysis of unreplicated factorials. Technometrics,31, 469–473.

    Google Scholar 

  5. Qiu, P., & Yandell, B. (1998). A local polynomial jump-detection algorithm in nonparametric regression. Technometrics,40, 141–152.

    Google Scholar 

  6. Sullivan, J. H. (2002). Detection of multiple change points from clustering individual observations. Journal of Quality Technology,34, 371–383.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Tokyo University of Science, 2641, Yamazaki, Noda, Chiba, 278-8510, Japan

    Seiichi Yasui, Hidehisa Noguchi & Yoshikazu Ojima

Authors
  1. Seiichi Yasui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hidehisa Noguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yoshikazu Ojima
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seiichi Yasui .

Editor information

Editors and Affiliations

  1. , Inst. Statistik und Ökonometrie, Freie Universität Berlin, Garystr. 21, Berlin, 14195, Berlin, Germany

    Hans-Joachim Lenz

  2. LS für Quantitative Methoden,, insbesondere Statistik, Europa-Universität Viadrina, Grosse Scharrnstr. 59, Frankfurt/Oder, 15230, Brandenburg, Germany

    Wolfgang Schmid

  3. , Inst. Statistik und Ökonometrie, Freie Universität Berlin, Garystr. 21, Berlin, 14195, Berlin, Germany

    Peter-Theodor Wilrich

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Yasui, S., Noguchi, H., Ojima, Y. (2012). A Robust Detection Procedure for Multiple Change Points of Linear Trends. In: Lenz, HJ., Schmid, W., Wilrich, PT. (eds) Frontiers in Statistical Quality Control 10. Frontiers in Statistical Quality Control, vol 10. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-2846-7_14

Download citation

Publish with us

Policies and ethics

Search

Navigation