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Multidimensional scaling used in multivariate statistical process control

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  • Trevor Cox

Abstract

This paper considers the use of multidimensional scaling techniques in multivariate statistical process control. Principal components analysis, multiple principal components analysis, partial least squares and PARAFAC models have already been established as useful methods for such, but it should be possible to widen the portfolio of techniques to include others that come under the multidimensional scaling class. Some of these are briefly described-namely classical scaling, non-metric scaling, biplots, Procrustes analysis-and are then used on some gas transportation data provided by Transco.

Suggested Citation

  • Trevor Cox, 2001. "Multidimensional scaling used in multivariate statistical process control," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(3-4), pages 365-378.
    • Handle: RePEc:taf:japsta:v:28:y:2001:i:3-4:p:365-378
    DOI: 10.1080/02664760120034108
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    References listed on IDEAS

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    1. Ross Sparks & Allan Adolphson & Aloke Phatak, 1997. "Multivariate Process Monitoring Using the Dynamic Biplot," International Statistical Review, International Statistical Institute, vol. 65(3), pages 325-349, December.
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    Cited by:

    1. Blanchard, Gilles & Kawanabe, Motoaki & Sugiyama, Masashi & Spokoiny, Vladimir & Müller, Klaus-Robert, 2006. "In search of non-Gaussian components of a high-dimensional distribution," SFB 649 Discussion Papers 2006-040, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. repec:hum:wpaper:sfb649dp2006-040 is not listed on IDEAS
    3. Antonis A. Michis, 2021. "Wavelet Multidimensional Scaling Analysis of European Economic Sentiment Indicators," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 443-480, October.
    4. repec:zbw:bofitp:2011_018 is not listed on IDEAS
    5. Wayne DeSarbo & Joonwook Park & Crystal Scott, 2008. "A Model-Based Approach for Visualizing the Dimensional Structure of Ordered Successive Categories Preference Data," Psychometrika, Springer;The Psychometric Society, vol. 73(1), pages 1-20, March.
    6. Sarlin, Peter & Peltonen, Tuomas A., 2013. "Mapping the state of financial stability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 46-76.
    7. Roberta Siciliano & Antonia D’Ambrosio & Massimo Aria & Sonia Amodio, 2016. "Analysis of Web Visit Histories, Part I: Distance-Based Visualization of Sequence Rules," Journal of Classification, Springer;The Classification Society, vol. 33(2), pages 298-324, July.
    8. Fei Cai & Honghui Chen & Zhen Shu, 2015. "Web document ranking via active learning and kernel principal component analysis," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 26(04), pages 1-18.

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