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p-value approximations for spatial scan statistics using extreme value distributions

Authors
 Inkyung Jung  ;  Goeun Park 
Citation
 STATISTICS IN MEDICINE, Vol.34(3) : 504-514, 2015 
Journal Title
 STATISTICS IN MEDICINE 
ISSN
 0277-6715 
Issue Date
2015
MeSH
Binomial Distribution ; Biometry/methods ; Breast Neoplasms/epidemiology ; Cluster Analysis* ; Computer Simulation ; Data Interpretation, Statistical* ; Humans ; Monte Carlo Method* ; Poisson Distribution ; Spatial Analysis* ; Texas/epidemiology
Keywords
Gumbel distribution ; Monte Carlo hypothesis testing ; generalized extreme value distribution
Abstract
Spatial scan statistics are widely applied to identify spatial clusters in geographic disease surveillance. To evaluate the statistical significance of detected clusters, Monte Carlo hypothesis testing is often used because the null distribution of spatial scan statistics is not known. A drawback of the method is that we have to increase the number of replications to obtain accurate p-values. Gumbel-based p-value approximations for spatial scan statistics have recently been proposed and evaluated for Poisson and Bernoulli models. In this study, we examine the use of a generalized extreme value distribution to approximate the null distribution of spatial scan statistics as well as the Gumbel distribution. Through simulation, p-value approximations using extreme value distributions for spatial scan statistics are assessed for multinomial and ordinal models in addition to Poisson and Bernoulli models.
Full Text
http://onlinelibrary.wiley.com/doi/10.1002/sim.6347/full
DOI
10.1002/sim.6347
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/22282913/139323
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