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Optimizing the maximum reported cluster size for normal-based spatial scan statistics

Authors
 Haerin Yoo  ;  Inkyung Jung 
Citation
 COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS, Vol.25(4) : 373-383, 2018 
Journal Title
 COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS 
ISSN
 2287-7843 
Issue Date
2018
Keywords
spatial cluster detection ; Gini coefficient ; maximum scanning window size ; weighted normal model
Abstract
The spatial scan statistic is a widely used method to detect spatial clusters. The method imposes a large number of scanning windows with pre-defined shapes and varying sizes on the entire study region. The likelihood ratio test statistic comparing inside versus outside each window is then calculated and the window with the maximum value of test statistic becomes the most likely cluster. The results of cluster detection respond sensitively to the shape and the maximum size of scanning windows. The shape of scanning window has been extensively studied; however, there has been relatively little attention on the maximum scanning window size (MSWS) or maximum reported cluster size (MRCS). The Gini coefficient has recently been proposed by Han et al. (International Journal of Health Geographics, 15, 27, 2016) as a powerful tool to determine the optimal value of MRCS for the Poisson-based spatial scan statistic. In this paper, we apply the Gini coefficient to normal-based spatial scan statistics. Through a simulation study, we evaluate the performance of the proposed method. We illustrate the method using a real data example of female colorectal cancer incidence rates in South Korea for the year 2009.
Full Text
http://www.csam.or.kr/journal/view.html?uid=1809&&vmd=Full
DOI
10.29220/CSAM.2018.25.4.373
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
Yonsei Authors
Jung, Inkyung(정인경) ORCID logo https://orcid.org/0000-0003-3780-3213
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/163213
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