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Evaluation of multivariate surveillance

Author

Listed:
  • Frisén, Marianne

    (Statistical Research Unit, Department of Economics, School of Business, Economics and Law, Göteborg University)

  • Andersson, Eva

    (Statistical Research Unit, Department of Economics, School of Business, Economics and Law, Göteborg University)

  • Schiöler, Linus

    (Statistical Research Unit, Department of Economics, School of Business, Economics and Law, Göteborg University)

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

  • Frisén, Marianne & Andersson, Eva & Schiöler, Linus, 2009. "Evaluation of multivariate surveillance," Research Reports 2009:1, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    • Handle: RePEc:hhs:gunsru:2009_001
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    File URL: http://hdl.handle.net/2077/20296
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    References listed on IDEAS

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    1. E. Andersson, 2002. "Monitoring cyclical processes. A non-parametric approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(7), pages 973-990.
    2. S. Knoth & W. Schmid, 2002. "Monitoring the mean and the variance of a stationary process," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 56(1), pages 77-100, February.
    3. 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.
    4. Christian Sonesson, 2003. "Evaluations of some Exponentially Weighted Moving Average methods," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1115-1133.
    5. Bersimis, Sotiris & Psarakis, Stelios & Panaretos, John, 2006. "Multivariate Statistical Process Control Charts: An Overview," MPRA Paper 6399, University Library of Munich, Germany.
    6. Clare Marshall & Nicky Best & Alex Bottle & Paul Aylin, 2004. "Statistical issues in the prospective monitoring of health outcomes across multiple units," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 541-559, August.
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    Citations

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    Cited by:

    1. Frisén, Marianne, 2011. "On multivariate control charts," Research Reports 2011:2, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Linus Schiöler & Marianne Fris�n, 2012. "Multivariate outbreak detection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 223-242, April.
    7. Frisén, Marianne, 2011. "Inference Principles For Multivariate Surveillance," Research Reports 2011:5, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.

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    More about this item

    Keywords

    average run length; delay; 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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    This paper has been announced in the following NEP Reports:

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