Semiparametric Surveillance of Monotonic Changes

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 "outbrea...

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Bibliographic Details
Published in:Sequential analysis 2009, Vol.28 (4), p.434-454
Main Authors: Frisén, Marianne, Andersson, Eva
Format: Article
Language:English
Subjects:
Change-points
Exponential family
Generalized likelihood
Monitoring
Ordered regression
Probability Theory and Statistics
Robust regression
Sannolikhetsteori och statistik
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Summary: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.
ISSN:0747-4946
1532-4176
DOI:10.1080/07474940903238029