Space-time stick-breaking processes for small area disease cluster estimation
DC Field | Value | Language |
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dc.contributor.author | Hossain, Md Monir | - |
dc.contributor.author | Lawson, Andrew B. | - |
dc.contributor.author | Cai, Bo | - |
dc.contributor.author | Choi, Jungsoon | - |
dc.contributor.author | Liu, Jihong | - |
dc.contributor.author | Kirby, Russell S. | - |
dc.date.accessioned | 2022-07-16T10:51:18Z | - |
dc.date.available | 2022-07-16T10:51:18Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2013-03 | - |
dc.identifier.issn | 1352-8505 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/163161 | - |
dc.description.abstract | We propose a space-time stick-breaking process for the disease cluster estimation. The dependencies for spatial and temporal effects are introduced by using space-time covariate dependent kernel stick-breaking processes. We compared this model with the space-time standard random effect model by checking each model's ability in terms of cluster detection of various shapes and sizes. This comparison was made for simulated data where the true risks were known. For the simulated data, we have observed that space-time stick-breaking process performs better in detecting medium- and high-risk clusters. For the real data, county specific low birth weight incidences for the state of South Carolina for the years 1997-2007, we have illustrated how the proposed model can be used to find grouping of counties of higher incidence rate. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Kluwer Academic Publishers | - |
dc.title | Space-time stick-breaking processes for small area disease cluster estimation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Choi, Jungsoon | - |
dc.identifier.doi | 10.1007/s10651-012-0209-0 | - |
dc.identifier.scopusid | 2-s2.0-84874794873 | - |
dc.identifier.wosid | 000316013600006 | - |
dc.identifier.bibliographicCitation | Environmental and Ecological Statistics, v.20, no.1, pp.91 - 107 | - |
dc.relation.isPartOf | Environmental and Ecological Statistics | - |
dc.citation.title | Environmental and Ecological Statistics | - |
dc.citation.volume | 20 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 91 | - |
dc.citation.endPage | 107 | - |
dc.type.rims | ART | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | cluster analysis | - |
dc.subject.keywordPlus | detection method | - |
dc.subject.keywordPlus | disease incidence | - |
dc.subject.keywordPlus | estimation method | - |
dc.subject.keywordPlus | model test | - |
dc.subject.keywordPlus | model validation | - |
dc.subject.keywordPlus | risk assessment | - |
dc.subject.keywordPlus | spatial variation | - |
dc.subject.keywordPlus | temporal variation | - |
dc.subject.keywordAuthor | Cluster | - |
dc.subject.keywordAuthor | Dependence | - |
dc.subject.keywordAuthor | Dirichlet process mixture | - |
dc.subject.keywordAuthor | Space-time | - |
dc.subject.keywordAuthor | Stick-breaking processes | - |
dc.identifier.url | https://link.springer.com/article/10.1007/s10651-012-0209-0 | - |
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