A nonparametric spatial scan statistic for continuous data.
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.


CONCLUSIONS
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.
Department of Biostatistics and Medical Informatics, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea. ijung@yuhs.ac.
Inkyung
I
Jung
Department of Biostatistics and Medical Informatics, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea. cherryjbr@daum.net.
Ho Jin
HJ
Cho
D016000:Cluster Analysis; D062305:Geographic Mapping; D006801:Humans; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods*; D018709:Statistics, Nonparametric