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http://dx.doi.org/10.5351/KJAS.2014.27.4.561

Spatial Clustering Method Via Generalized Lasso  

Song, Eunjung (Department of Statistics, Inha University)
Choi, Hosik (Department of Applied Information Statistics, Kyonggi University)
Hwang, Seungsik (College of Medicine, Inha University)
Lee, Woojoo (Department of Statistics, Inha University)
Publication Information
The Korean Journal of Applied Statistics / v.27, no.4, 2014 , pp. 561-575 More about this Journal
Abstract
In this paper, we propose a penalized likelihood method to detect local spatial clusters associated with disease. The key computational algorithm is based on genlasso by Tibshirani and Taylor (2011). The proposed method has two main advantages over Kulldorff's method which is popoular to detect local spatial clusters. First, it is not needed to specify a proper cluster size a priori. Second, any type of covariate can be incorporated and, it is possible to find local spatial clusters adjusted for some demographic variables. We illustrate our proposed method using tuberculosis data from Seoul.
Keywords
Spatial clustering; generalized lasso; lasso; fused lasso;
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