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 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 efficient methods. The sufficiency principle will be used to determine efficient methods to combine data from sources with different time lag. The technique is used at the surveillance of outbreaks of influenza in Sweden. A method for robust semiparametric outbreak detection is generalized for spatial data. References are found at www.statistics.gu.se/surveillance.
Keywords: Sequential; Surveillance; Multivariate; Sufficiency
Biography: Marianne Frisén is professor emerita at Statistical Research Unit, University of Gothenburg, Sweden. Her main interests are in statistical surveillance, the foundations of statistical inference, robust methods, order restricted inference and applied work.