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Discussion on the paper by Peter Muller, Fernando A. Quintana, and Garritt L. Page
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jo, Seongil | - |
dc.contributor.author | Lee, Jaeyong | - |
dc.date.accessioned | 2023-10-30T01:50:58Z | - |
dc.date.available | 2023-10-30T01:50:58Z | - |
dc.date.created | 2019-03-20 | - |
dc.date.issued | 2018-06 | - |
dc.identifier.citation | Statistical Methods and Applications, Vol.27 No.2, pp.227-230 | - |
dc.identifier.issn | 1618-2510 | - |
dc.identifier.uri | https://hdl.handle.net/10371/195916 | - |
dc.description.abstract | The article by Muller, Quintana, and Page reviews a variety of Bayesian nonparametric models and demonstrates them in a few applications. They emphasize applications in spatial data on which our discussion focuses as well. In particular, we consider two types of mixture models based on species sampling models (SSM) for spatial clustering and apply them to the Chilean mathematics testing score data analyzed by the authors. We conclude that only the mixture model of SSM with spatial locations as part of observations renders spatially non-overlapping clusters. | - |
dc.language | 영어 | - |
dc.publisher | Springer Verlag | - |
dc.title | Discussion on the paper by Peter Muller, Fernando A. Quintana, and Garritt L. Page | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/s10260-017-0396-9 | - |
dc.citation.journaltitle | Statistical Methods and Applications | - |
dc.identifier.wosid | 000433538700004 | - |
dc.identifier.scopusid | 2-s2.0-85028980896 | - |
dc.citation.endpage | 230 | - |
dc.citation.number | 2 | - |
dc.citation.startpage | 227 | - |
dc.citation.volume | 27 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Lee, Jaeyong | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordPlus | SPECIES SAMPLING MODELS | - |
dc.subject.keywordAuthor | Spatial clustering | - |
dc.subject.keywordAuthor | Spatial correlation structure | - |
dc.subject.keywordAuthor | Species sampling mixture models | - |
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