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Cited 7 time in webofscience Cited 6 time in scopus
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dc.contributor.authorCHAE, MINWOO-
dc.contributor.authorRyan Martin-
dc.contributor.authorStephen G. Walker-
dc.date.accessioned2019-06-04T00:30:07Z-
dc.date.available2019-06-04T00:30:07Z-
dc.date.created2019-06-03-
dc.date.issued2019-07-
dc.identifier.issn0960-3174-
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/99020-
dc.description.abstractIn this paper we use an iterative algorithm for solving Fredholm equations of the first kind. The basic algorithm and convergence properties are known under certain conditions, but we provide a simpler convergence proof without requiring the restrictive conditions that have previously been needed. Several examples of independent interest are given, including mixing density estimation and a first passage time density function involving Brownian motion. We also develop the basic algorithm to include functions which are not necessarily non-negative and, again, present illustrations.-
dc.languageEnglish-
dc.publisherKluwer Academic Publishers-
dc.relation.isPartOfStatistics and Computing-
dc.titleOn an algorithm for solving Fredholm integrals of the first kind-
dc.typeArticle-
dc.identifier.doi10.1007/s11222-018-9829-z-
dc.type.rimsART-
dc.identifier.bibliographicCitationStatistics and Computing, v.29, no.4, pp.645 - 654-
dc.identifier.wosid000469249200003-
dc.citation.endPage654-
dc.citation.number4-
dc.citation.startPage645-
dc.citation.titleStatistics and Computing-
dc.citation.volume29-
dc.contributor.affiliatedAuthorCHAE, MINWOO-
dc.identifier.scopusid2-s2.0-85053255692-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.type.docTypeArticle-
dc.subject.keywordAuthorBrownian motion first passage time-
dc.subject.keywordAuthorConvergence-
dc.subject.keywordAuthorExpectation-maximization-
dc.subject.keywordAuthorIterative algorithm-
dc.subject.keywordAuthorMixture model-
dc.relation.journalWebOfScienceCategoryComputer Science, Theory & Methods-
dc.relation.journalWebOfScienceCategoryStatistics & Probability-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-

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