Spatial multiresolution cluster detection method Zhang, Lingsong Zhu, Zhengyuan Zhu, Zhengyuan
dc.contributor.department Statistics
dc.contributor.department Center for Survey Statistics and Methodology (CSSM) 2020-06-14T01:09:38.000 2020-07-02T06:57:45Z 2020-07-02T06:57:45Z Tue Jan 01 00:00:00 UTC 2013 2013-01-01
dc.description.abstract <p>A novel multi-resolution cluster detection (MCD) method is proposed to identify irregularly shaped clusters in space. Multi-scale test statistic on a single cell is derived based on likelihood ratio statistic for Bernoulli sequence, Poisson sequence and Normal sequence. A neighborhood variability measure is defined to select the optimal test threshold. The MCD method is compared with single scale testing methods controlling for false discovery rate and the spatial scan statistics using simulation and f-MRI data. The MCD method is shown to be more effective for discovering irregularly shaped clusters, and the implementation of this method does not require heavy computation, making it suitable for cluster detection for large spatial data.</p>
dc.description.comments <p>This is a manuscript of an article published as Zhang, Lingsong, and Zhengyuan Zhu. "Spatial multiresolution cluster detection method." <em>Statistics and Its Interface</em> 6, no. 1 (2013): 65-77. DOI: <a href="" target="_blank">10.4310/SII.2013.v6.n1.a7</a>. Posted with permission.</p>
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dc.identifier archive/
dc.identifier.articleid 1302
dc.identifier.contextkey 18090375
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/300
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 23:27:59 UTC 2022
dc.source.uri 10.4310/SII.2013.v6.n1.a7
dc.subject.disciplines Statistics and Probability
dc.subject.keywords multiresolution
dc.subject.keywords scan statistics
dc.subject.keywords scale space inference
dc.subject.keywords spatial test
dc.title Spatial multiresolution cluster detection method
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 51db2a08-8f9d-4f97-bdbc-6790b3d5a608
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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