Bayesian inference for the proportion of true null hypotheses using minimum Hellinger distance
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, Moonsu | - |
dc.contributor.author | Lee, Jaewon | - |
dc.date.accessioned | 2021-09-06T21:57:33Z | - |
dc.date.available | 2021-09-06T21:57:33Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2012-04 | - |
dc.identifier.issn | 0378-3758 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/108899 | - |
dc.description.abstract | It is important that the proportion of true null hypotheses be estimated accurately in a multiple hypothesis context. Current estimation methods, however, are not suitable for high-dimensional data such as microarray data. First, they do not consider the (strong) dependence between hypotheses (or genes), thereby resulting in inaccurate estimation. Second, the unknown distribution of false null hypotheses cannot be estimated properly by these methods. Third, the estimation is affected strongly by outliers. In this paper, we find out the optimal procedure for estimating the proportion of true null hypotheses under a (strong) dependence based on the Dirichlet process prior. In addition, by using the minimum Hellinger distance, the estimation should be robust to any model misspecification as well as to any outliers while maintaining efficiency. The results are confirmed by a simulation study, and the newly developed methodology is illustrated by a real microarray data. (C) 2011 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.subject | MARGINAL LIKELIHOOD | - |
dc.subject | MODELS | - |
dc.subject | DISCOVERY | - |
dc.subject | ROBUSTNESS | - |
dc.subject | EFFICIENCY | - |
dc.subject | NORMALITY | - |
dc.subject | OUTPUT | - |
dc.subject | TESTS | - |
dc.title | Bayesian inference for the proportion of true null hypotheses using minimum Hellinger distance | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Jaewon | - |
dc.identifier.doi | 10.1016/j.jspi.2011.10.001 | - |
dc.identifier.scopusid | 2-s2.0-84866306601 | - |
dc.identifier.wosid | 000299856500005 | - |
dc.identifier.bibliographicCitation | JOURNAL OF STATISTICAL PLANNING AND INFERENCE, v.142, no.4, pp.820 - 825 | - |
dc.relation.isPartOf | JOURNAL OF STATISTICAL PLANNING AND INFERENCE | - |
dc.citation.title | JOURNAL OF STATISTICAL PLANNING AND INFERENCE | - |
dc.citation.volume | 142 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 820 | - |
dc.citation.endPage | 825 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | MARGINAL LIKELIHOOD | - |
dc.subject.keywordPlus | MODELS | - |
dc.subject.keywordPlus | DISCOVERY | - |
dc.subject.keywordPlus | ROBUSTNESS | - |
dc.subject.keywordPlus | EFFICIENCY | - |
dc.subject.keywordPlus | NORMALITY | - |
dc.subject.keywordPlus | OUTPUT | - |
dc.subject.keywordPlus | TESTS | - |
dc.subject.keywordAuthor | Proportion of true null | - |
dc.subject.keywordAuthor | Strong dependence | - |
dc.subject.keywordAuthor | Dirichlet process | - |
dc.subject.keywordAuthor | Minimum Hellinger distance | - |
dc.subject.keywordAuthor | IWMDE | - |
dc.subject.keywordAuthor | Microarray | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.