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Evaluation of multivariate surveillance

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  • Marianne Frisen
  • Eva Andersson
  • Linus Schioler

Abstract

Multivariate surveillance is of interest in many areas such as industrial production, bioterrorism detection, spatial surveillance, and financial transaction strategies. Some of the suggested approaches to multivariate surveillance have been multivariate counterparts to the univariate Shewhart, EWMA, and CUSUM methods. Our emphasis is on the special challenges of evaluating multivariate surveillance methods. Some new measures are suggested and the properties of several measures are demonstrated by applications to various situations. It is demonstrated that zero-state and steady-state ARL, which are widely used in univariate surveillance, should be used with care in multivariate surveillance.

Suggested Citation

  • Marianne Frisen & Eva Andersson & Linus Schioler, 2010. "Evaluation of multivariate surveillance," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(12), pages 2089-2100.
    • Handle: RePEc:taf:japsta:v:37:y:2010:i:12:p:2089-2100
    DOI: 10.1080/02664760903222208
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    References listed on IDEAS

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    1. David Bock, 2008. "Aspects on the control of false alarms in statistical surveillance and the impact on the return of financial decision systems," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(2), pages 213-227.
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    Citations

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    Cited by:

    1. Marianne Frisén, 2014. "Spatial outbreak detection based on inference principles for multivariate surveillance," IISE Transactions, Taylor & Francis Journals, vol. 46(8), pages 759-769, August.
    2. Linus Schiöler & Marianne Fris�n, 2012. "Multivariate outbreak detection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 223-242, April.
    3. Frisén, Marianne, 2011. "Inference Principles For Multivariate Surveillance," Research Reports 2011:5, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    4. Frisén, Marianne, 2011. "On multivariate control charts," Research Reports 2011:2, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    5. Jonsson, Robert, 2011. "Simple conservative confidence intervals for comparing matched proportions," Research Reports 2011:1, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    6. Golosnoy, Vasyl & Ragulin, Sergiy & Schmid, Wolfgang, 2011. "CUSUM control charts for monitoring optimal portfolio weights," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2991-3009, November.
    7. Schiöler, Linus, 2010. "Modelling the spatial patterns of influenza incidence in Sweden," Research Reports 2010:1, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.

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    More about this item

    Keywords

    average run length; EWMA; false alarms; FDR; performance metrics; predictive value; steady state; zero state;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

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