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- W3210906757 abstract "Spatial and spatio-temporal cluster detection are important tools in public health and many other areas of application. Cluster detection can be approached as a multiple testing problem, typically using a space and time scan statistic. We recast the spatial and spatio-temporal cluster detection problem in a high-dimensional data analytical framework with Poisson or quasi-Poisson regression with the Lasso penalty. We adopt a fast and computationally-efficient method using a novel sparse matrix representation of the effects of potential clusters. The number of clusters and tuning parameters are selected based on (quasi-)information criteria. We evaluate the performance of our proposed method including the false positive detection rate and power using a simulation study. Application of the method is illustrated using breast cancer incidence data from three prefectures in Japan." @default.
- W3210906757 created "2021-11-08" @default.
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- W3210906757 date "2022-06-01" @default.
- W3210906757 modified "2023-10-01" @default.
- W3210906757 title "Regularized spatial and spatio-temporal cluster detection" @default.
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- W3210906757 doi "https://doi.org/10.1016/j.sste.2021.100462" @default.
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