Semiparametric Surveillance of Monotonic Changes |
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| Authors: | Marianne Frisén Eva Andersson |
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| Affiliation: | 1. Statistical Research Unit, Department of Economics , University of Gothenburg , Gothenburg, Sweden Marianne.Frisen@statistics.gu.se;3. Statistical Research Unit, Department of Economics , University of Gothenburg , Gothenburg, Sweden;4. Department of Occupational and Environmental Medicine , Sahlgrenska Academy and Sahlgrenska University Hospital, University of Gothenburg , Gothenburg, Sweden |
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| Abstract: | Abstract The detection of a change from a constant level to a monotonically increasing (or decreasing) regression is of special interest for the detection of outbreaks of epidemics but is also of interest in other areas. A maximum likelihood ratio statistic for the sequential surveillance of an “outbreak” situation is derived. The method is semiparametric in the sense that the regression model is nonparametric whereas the distribution belongs to the regular exponential family. The method is evaluated with respect to timeliness and predicted value in a simulation study that imitates the influenza outbreaks in Sweden. To illustrate its performance, the method is applied to Swedish influenza data for 6 years. The advantage of this semiparametric surveillance method, which does not rely on an estimated baseline, is illustrated by a Monte Carlo study. The advantage of information accumulation is illustrated. |
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| Keywords: | Change-points Exponential family Generalized likelihood Monitoring Ordered regression Robust regression |
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