Inference Principles For Multivariate Surveillance
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.
|Date of creation:||29 Mar 2011|
|Publication status:||Forthcoming as Frisén, Marianne, 'INFERENCE PRINCIPLES FOR MULTIVARIATE SURVEILLANCE' in Calcutta Statistical Association Bulletin, 2012.|
|Contact details of provider:|| Postal: Statistical Research Unit, University of Gothenburg, Box 640, SE 40530 GÖTEBORG|
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- S. Knoth, 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.
- 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.
- Marianne Frisen & Eva Andersson & Linus Schioler, 2010. "Evaluation of multivariate surveillance," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(12), pages 2089-2100.
- 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.
- 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.
- 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.
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