Sufficient Reduction in Multivariate Surveillance

Frisén, Marianne; Andersson, Eva; Schiöler, Linus

doi:10.1080/03610921003714162

Cited by 15 publications

(29 citation statements)

References 35 publications

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“…Wessman (1998) and Frisén et al (2011) demonstrated that if all processes have the same change points, then the univariate vector of partial likelihood ratios {L(s,t), t=1,...s}, for τ<s against τ=s, is sufficient for the sequence of distributional families. Thus, in order to monitor a simultaneous and fully specified change, it is possible to construct a univariate surveillance procedure based on the sufficient sequence of likelihood ratios.…”

Section: Simultaneous Change At All Locationsmentioning

confidence: 99%

Spatial outbreak detection based on inference principles for multivariate surveillance

Frisén¹

2014

IIE Transactions

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Section: Simultaneous Change At All Locationsmentioning

confidence: 99%

Spatial outbreak detection based on inference principles for multivariate surveillance

Frisén¹

2014

IIE Transactions

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“…Another example is the spatial spread of a disease as will be described in Section 3.5. Sufficient reduction for a step change is derived in Frisén et al (2011) and for a semiparametric model in Schiöler and Frisén (2010).…”

Section: Changes With Time Lagsmentioning

confidence: 99%

Inference Principles for Multivariate Surveillance

Frisén

2011

Calcutta Statistical Association Bulletin

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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.

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“…Frisén, et al (2010c) studied the case of changes in the general one-parameter exponential family (including the Poisson distribution) but also only for step changes. Different levels of the parameter before the change as well as differences in shift size were considered.…”

Section: Changes With a Time Lag Between Locationsmentioning

confidence: 99%

“…When all processes change at the start it follows that they change simultaneously. Wessman (1998) andFrisén, et al (2010c) demonstrated that if all processes have the same change points, i.e. τ 1 = τ 2 =...τ p =τ, then the univariate vector of partial likelihood ratios, {L(s,t), t=1,...s} where ( , ) (…”

Section: Simultaneous Change At All Locationsmentioning

confidence: 99%

“…In Section 3, we will derive a sufficient reduction of the data for multivariate outbreak situations. Sufficient reduction for detection of step changes was earlier derived by Frisén et al (2010c) but here it is derived for detection of gradual outbreaks. In Section 4, we will discuss general approaches of how multivariate surveillance can be constructed from univariate surveillance, and construct a simple multivariate outbreak detection method, based on the univariate method by .…”

Section: Introductionmentioning

confidence: 99%

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Multivariate outbreak detection

Schiöler

Frisén

2012

Journal of Applied Statistics

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On-line monitoring is needed to detect outbreaks of diseases like influenza. Surveillance is also needed for other kinds of outbreaks, in the sense of an increasing expected value after a constant period. Information on spatial location or other variables might be available and may be utilized. We adapted a robust method for outbreak detection to a multivariate case. The relation between the times of the onsets of the outbreaks at different locations (or some other variable) was used to determine the sufficient statistic for surveillance. The derived maximum likelihood estimator of the outbreak regression was semi-parametric in the sense that the baseline and the slope were nonparametric while the distribution belonged to the exponential family. The estimator was used in a generalized likelihood ratio surveillance method. The method was evaluated with respect to robustness and efficiency in a simulation study and applied to spatial data for detection of influenza outbreaks in Sweden.

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Sufficient Reduction in Multivariate Surveillance

Cited by 15 publications

References 35 publications

Spatial outbreak detection based on inference principles for multivariate surveillance

Spatial outbreak detection based on inference principles for multivariate surveillance

Inference Principles for Multivariate Surveillance

Multivariate outbreak detection

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