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Inference Principles For Multivariate Surveillance

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  • Frisén, Marianne

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

Abstract

Multivariate surveillance is of interest in industrial production as it enables the monitoring of several components. Recently there has been an increased interest also in other areas such as detection of bioterrorism, spatial surveillance and transaction strategies in finance. Multivariate counterparts to the univariate Shewhart, EWMA and CUSUM methods have earlier been proposed. A review of general approaches to multivariate surveillance is given with respect to how suggested methods relate to general statistical inference principles. Multivariate on-line surveillance problems can be complex. The sufficiency principle can be of great use to find simplifications without loss of information. We will use this to clarify the structure of some problems. This will be of help to find relevant metrics for evaluations of multivariate surveillance and to find optimal methods. The sufficiency principle will be used to determine efficient methods to combine data from sources with different time lag. Surveillance of spatial data is one example. Illustrations will be given of surveillance of outbreaks of influenza.

Suggested Citation

  • Frisén, Marianne, 2011. "Inference Principles For Multivariate Surveillance," Research Reports 2011:5, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    • Handle: RePEc:hhs:gunsru:2011_005
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    File URL: http://gupea.ub.gu.se/handle/2077/25008
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    References listed on IDEAS

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    1. Sven Knoth & Marianne Frisén, 2012. "Minimax optimality of CUSUM for an autoregressive model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(4), pages 357-379, November.
    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. Marianne Frisén, 2003. "Statistical Surveillance. Optimality and Methods," International Statistical Review, International Statistical Institute, vol. 71(2), pages 403-434, August.
    4. 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.
    5. Marianne Frisen & Eva Andersson & Linus Schioler, 2010. "Evaluation of multivariate surveillance," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(12), pages 2089-2100.
    6. Arthur Yeh & Dennis Lin & Honghong Zhou & Chandramouliswaran Venkataramani, 2003. "A multivariate exponentially weighted moving average control chart for monitoring process variability," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(5), pages 507-536.
    7. Frisén, Marianne, 2011. "Methods and evaluations for surveillance in industry, business, finance, and public health," Research Reports 2011:3, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    8. Vasyl Golosnoy, 2007. "Sequential monitoring of minimum variance portfolio," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 91(1), pages 39-55, March.
    9. Frisén, Marianne, 2011. "On multivariate control charts," Research Reports 2011:2, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    10. 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.
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    More about this item

    Keywords

    Sequential; Surveillance; Multivariate; Sufficiency;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

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    This paper has been announced in the following NEP Reports:

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