A one‐sided MEWMA chart for health surveillance

Joner, M. D.; Woodall, William H.; Reynolds, Marion R.; Fricker, Ronald D.

doi:10.1002/qre.910

Cited by 63 publications

(33 citation statements)

References 39 publications

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“…Often the goal in applications such as biosurveillance is to detect "signed" change, such as increases in reported incidents of an infectious disease. Although several approaches to developing directionally sensitive multivariate procedures have been considered (Fricker 2007;Fricker, Knitt, and Hu 2008;Joner et al 2008), existing graph-theoretic procedures are not designed for this setting.…”

Section: Future Workmentioning

confidence: 99%

Nonparametric Tests for Homogeneity Based on Non-Bipartite Matching

Ruth

Koyak

2011

Journal of the American Statistical Association

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Given a sequence of observations, has a change occurred in the underlying probability distribution with respect to observation order? This problem of detecting change points arises in a variety of applications including health prognostics for mechanical systems, syndromic disease surveillance in geographically dispersed populations, anomaly detection in information networks, and multivariate process control in general. Detecting change points in high-dimensional settings is challenging, and most change-point methods for multidimensional problems rely upon distributional assumptions or the use of observation history to model probability distributions. We present three new nonparametric statistical tests for heterogeneity based on the combinatorial properties of minimum non-bipartite matching (MNBM). The key idea underlying each of these tests is that if a sequence of independent random observations undergoes a change in distribution-either an abrupt "shift" or a gradual "drift"-a MNBM based on inter-point distances tends to produce pairings that are closer in the sequence labeling than would be the case if the observations were drawn from the same distribution. Our tests follow on the work of Rosenbaum (2005) who used MNBM to derive a simple cross-match test statistic for the two-sample problem based on this idea. Similar ideas are present in the minimum spanning tree (MST) test derived by Rafsky (1979, 1981). We extend these approaches by utilizing ensembles of orthogonal MNBMs which greatly increase information extraction from the data, leading to tests that compare favorably to parametric procedures while maintaining level and good power properties across distributions.

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Section: Future Workmentioning

confidence: 99%

Nonparametric Tests for Homogeneity Based on Non-Bipartite Matching

Ruth

Koyak

2011

Journal of the American Statistical Association

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“…Spatial surveillance is a special case of multivariate surveillance, as pointed out for example by Sonesson and Frisén (2005) and Joner Jr. et al (2008).…”

Section: Introductionmentioning

confidence: 99%

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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“…Some control chart methods have also been proposed such as the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart method proposed by Joner et al (2008). This approach could be extended to the lattice structure of counts discussed below but involving all cell counts in the lattice making it a high dimensional application of the MEWMA plan which is difficult to implement.…”

Section: Introductionmentioning

confidence: 99%