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Article: Direct deconvolution density estimation of a mixture distribution motivated by mutation effects distribution

TitleDirect deconvolution density estimation of a mixture distribution motivated by mutation effects distribution
Authors
KeywordsDiscrete component
Deconvolution
Virus fitness
Mixture distribution
Measurement error
Issue Date2010
Citation
Journal of Nonparametric Statistics, 2010, v. 22, n. 1, p. 1-22 How to Cite?
AbstractThe mutation effect distribution is essential for understanding evolutionary dynamics. However, the existing studies on this problem have had limited resolution. So far, the most widely used method is to fit some parametric distribution, such as an exponential distribution whose validity has not been checked. In this paper, we propose a nonparametric density estimator for the mutation effect distribution, based on a deconvolution approach. Consistency of the estimator is also established. Unlike the existing deconvolution estimators, we cover the case that the target variable has a mixture structure with a pointmass and a continuous component. To study the property of the proposed estimator, several simulation studies are performed. In addition, an application for modelling virus mutation effects is provided. © American Statistical Association and Taylor & Francis 2010.
Persistent Identifierhttp://hdl.handle.net/10722/219629
ISSN
2015 Impact Factor: 0.446
2015 SCImago Journal Rankings: 0.980

 

DC FieldValueLanguage
dc.contributor.authorLee, Mihee-
dc.contributor.authorShen, Haipeng-
dc.contributor.authorBurch, Christina-
dc.contributor.authorMarron, J. S.-
dc.date.accessioned2015-09-23T02:57:34Z-
dc.date.available2015-09-23T02:57:34Z-
dc.date.issued2010-
dc.identifier.citationJournal of Nonparametric Statistics, 2010, v. 22, n. 1, p. 1-22-
dc.identifier.issn1048-5252-
dc.identifier.urihttp://hdl.handle.net/10722/219629-
dc.description.abstractThe mutation effect distribution is essential for understanding evolutionary dynamics. However, the existing studies on this problem have had limited resolution. So far, the most widely used method is to fit some parametric distribution, such as an exponential distribution whose validity has not been checked. In this paper, we propose a nonparametric density estimator for the mutation effect distribution, based on a deconvolution approach. Consistency of the estimator is also established. Unlike the existing deconvolution estimators, we cover the case that the target variable has a mixture structure with a pointmass and a continuous component. To study the property of the proposed estimator, several simulation studies are performed. In addition, an application for modelling virus mutation effects is provided. © American Statistical Association and Taylor & Francis 2010.-
dc.languageeng-
dc.relation.ispartofJournal of Nonparametric Statistics-
dc.subjectDiscrete component-
dc.subjectDeconvolution-
dc.subjectVirus fitness-
dc.subjectMixture distribution-
dc.subjectMeasurement error-
dc.titleDirect deconvolution density estimation of a mixture distribution motivated by mutation effects distribution-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1080/10485250903085847-
dc.identifier.scopuseid_2-s2.0-77953481427-
dc.identifier.volume22-
dc.identifier.issue1-
dc.identifier.spage1-
dc.identifier.epage22-
dc.identifier.eissn1029-0311-

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