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Article: Least squares sieve estimation of mixture distributions with boundary effects

TitleLeast squares sieve estimation of mixture distributions with boundary effects
Authors
KeywordsMixture distribution
Measurement error
Least squares
Sieve method
Secondary
Primary
Penalization
Issue Date2015
Citation
Journal of the Korean Statistical Society, 2015, v. 44 n. 2, p. 187-201 How to Cite?
AbstractIn this study, we propose two types of sieve estimators, based on least squares (LS), for probability distributions that are mixtures of a finite number of discrete atoms and a continuous distribution under the framework of measurement error models. This research is motivated by the maximum likelihood (ML) sieve estimator developed in Lee etal.(2013). We obtain two types of LS sieve estimators through minimizing the distance between the empirical distribution/characteristic functions and the model distribution/characteristic functions. The LS estimators outperform the ML sieve estimator in several aspects: (1) they need much less computational time; (2) they give smaller integrated mean squared error; (3) the characteristic function based LS estimator is more robust against mis-specification of the error distribution. We also use roughness penalization to improve the smoothness of the resulting estimators and reduce the estimation variance. As an application of our proposed LS estimators, we use the Framingham Heart Study data to investigate the distribution of genetic effects on body mass index. Finally asymptotic properties of the LS estimators are investigated. © 2014 The Korean Statistical Society.
Persistent Identifierhttp://hdl.handle.net/10722/219756
ISSN
2015 Impact Factor: 0.353
2015 SCImago Journal Rankings: 0.392

 

DC FieldValueLanguage
dc.contributor.authorLee, Mihee-
dc.contributor.authorWang, Ling-
dc.contributor.authorShen, Haipeng-
dc.contributor.authorHall, Peter-
dc.contributor.authorGuo, Guang-
dc.contributor.authorMarron, J. S.-
dc.date.accessioned2015-09-23T02:57:53Z-
dc.date.available2015-09-23T02:57:53Z-
dc.date.issued2015-
dc.identifier.citationJournal of the Korean Statistical Society, 2015, v. 44 n. 2, p. 187-201-
dc.identifier.issn1226-3192-
dc.identifier.urihttp://hdl.handle.net/10722/219756-
dc.description.abstractIn this study, we propose two types of sieve estimators, based on least squares (LS), for probability distributions that are mixtures of a finite number of discrete atoms and a continuous distribution under the framework of measurement error models. This research is motivated by the maximum likelihood (ML) sieve estimator developed in Lee etal.(2013). We obtain two types of LS sieve estimators through minimizing the distance between the empirical distribution/characteristic functions and the model distribution/characteristic functions. The LS estimators outperform the ML sieve estimator in several aspects: (1) they need much less computational time; (2) they give smaller integrated mean squared error; (3) the characteristic function based LS estimator is more robust against mis-specification of the error distribution. We also use roughness penalization to improve the smoothness of the resulting estimators and reduce the estimation variance. As an application of our proposed LS estimators, we use the Framingham Heart Study data to investigate the distribution of genetic effects on body mass index. Finally asymptotic properties of the LS estimators are investigated. © 2014 The Korean Statistical Society.-
dc.languageeng-
dc.relation.ispartofJournal of the Korean Statistical Society-
dc.subjectMixture distribution-
dc.subjectMeasurement error-
dc.subjectLeast squares-
dc.subjectSieve method-
dc.subjectSecondary-
dc.subjectPrimary-
dc.subjectPenalization-
dc.titleLeast squares sieve estimation of mixture distributions with boundary effects-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.jkss.2014.07.003-
dc.identifier.scopuseid_2-s2.0-84905308878-
dc.identifier.hkuros263851-
dc.identifier.volume44-
dc.identifier.issue2-
dc.identifier.spage187-
dc.identifier.epage201-

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