INFERENCE PRINCIPLES FOR MULTIVARIATE SURVEILLANCE
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.
Keywords: Sequential; Surveillance; Multivariate; Sufficiency; (follow links to similar papers)
JEL-Codes: C44; (follow links to similar papers)
15 pages, March 29, 2011
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- This paper is forthcoming as:
Frisén, Marianne, 'INFERENCE PRINCIPLES FOR MULTIVARIATE SURVEILLANCE', Calcutta Statistical Association Bulletin.
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