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Article: A bootstrap procedure for local semiparametric density estimation amid model uncertainties

TitleA bootstrap procedure for local semiparametric density estimation amid model uncertainties
Authors
Issue Date2014
Publishersciencedirect. The Journal's web site is located at http://www.elsevier.com/locate/jspi
Citation
Journal of Statistical Planning and Inference, 2014, v. 153, p. 75-86 How to Cite?
AbstractWe revisit a semiparametric procedure for density estimation based on a convex combination of a nonparametric kernel density estimator and a parametric maximum likelihood estimator, with the mixing weight locally estimated by the bootstrap method. We establish the asymptotic properties of the resulting semiparametric estimator, and show that undersmoothing at the bootstrap step is necessary if the estimator is to attain a convergence rate faster than that of the kernel density estimator under a good local parametric fit. A simulation study is conducted to investigate the finite-sample performance of the procedure. Exploiting its adaptivity to the goodness of local parametric fit, we propose a double bootstrap algorithm to incorporate into the semiparametric procedure more than one parametric family, and illustrate with a numerical example the benefits gained thereof.
Persistent Identifierhttp://hdl.handle.net/10722/200912

 

DC FieldValueLanguage
dc.contributor.authorSoleymani, Men_US
dc.contributor.authorLee, SMSen_US
dc.date.accessioned2014-08-21T07:07:08Z-
dc.date.available2014-08-21T07:07:08Z-
dc.date.issued2014en_US
dc.identifier.citationJournal of Statistical Planning and Inference, 2014, v. 153, p. 75-86en_US
dc.identifier.urihttp://hdl.handle.net/10722/200912-
dc.description.abstractWe revisit a semiparametric procedure for density estimation based on a convex combination of a nonparametric kernel density estimator and a parametric maximum likelihood estimator, with the mixing weight locally estimated by the bootstrap method. We establish the asymptotic properties of the resulting semiparametric estimator, and show that undersmoothing at the bootstrap step is necessary if the estimator is to attain a convergence rate faster than that of the kernel density estimator under a good local parametric fit. A simulation study is conducted to investigate the finite-sample performance of the procedure. Exploiting its adaptivity to the goodness of local parametric fit, we propose a double bootstrap algorithm to incorporate into the semiparametric procedure more than one parametric family, and illustrate with a numerical example the benefits gained thereof.en_US
dc.languageengen_US
dc.publishersciencedirect. The Journal's web site is located at http://www.elsevier.com/locate/jspien_US
dc.relation.ispartofJournal of Statistical Planning and Inferenceen_US
dc.titleA bootstrap procedure for local semiparametric density estimation amid model uncertaintiesen_US
dc.typeArticleen_US
dc.identifier.emailSoleymani, M: mehdi@hku.hken_US
dc.identifier.emailLee, SMS: smslee@hku.hken_US
dc.identifier.authorityLee, SMS=rp00726en_US
dc.identifier.doi10.1016/j.jspi.2014.05.004en_US
dc.identifier.hkuros231943en_US
dc.identifier.volume153en_US
dc.identifier.spage75en_US
dc.identifier.epage86en_US
dc.publisher.placeNORTH-HOLLANDen_US

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