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A nonparametric spatial scan statistic for continuous data

Authors
 Inkyung Jung  ;  Ho Jin Cho 
Citation
 INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS, Vol.14 : 30, 2015 
Journal Title
 INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS 
Issue Date
2015
MeSH
Cluster Analysis ; Geographic Mapping* ; Humans ; Models, Statistical ; Population Surveillance/methods* ; Statistics, Nonparametric*
Keywords
Spatial cluster detection test ; Normal-based scan statistic ; Wilcoxon rank-sum test
Abstract
BACKGROUND: Spatial scan statistics are widely used for spatial cluster detection, and several parametric models exist. For continuous data, a normal-based scan statistic can be used. However, the performance of the model has not been fully evaluated for non-normal data. METHODS: We propose a nonparametric spatial scan statistic based on the Wilcoxon rank-sum test statistic and compared the performance of the method with parametric models via a simulation study under various scenarios. RESULTS: The nonparametric method outperforms the normal-based scan statistic in terms of power and accuracy in almost all cases under consideration in the simulation study. CONCLUSION: The proposed nonparametric spatial scan statistic is therefore an excellent alternative to the normal model for continuous data and is especially useful for data following skewed or heavy-tailed distributions.
Files in This Item:
T201504102.pdf Download
DOI
10.1186/s12942-015-0024-6
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/22282913/141651
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