Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/24054
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dc.contributor.authorJANSSEN, Paul-
dc.contributor.authorSwanepoel, Jan-
dc.contributor.authorVERAVERBEKE, Noel-
dc.date.accessioned2017-08-03T07:34:30Z-
dc.date.available2017-08-03T07:34:30Z-
dc.date.issued2017-
dc.identifier.citationJOURNAL OF MULTIVARIATE ANALYSIS, 159, p. 39-48-
dc.identifier.issn0047-259X-
dc.identifier.urihttp://hdl.handle.net/1942/24054-
dc.description.abstractSome recent papers deal with smooth nonparametric estimators for copula functions and copula derivatives. These papers contain results on copula-based Bernstein estimators for conditional distribution functions and related functionals such as regression and quantile functions. The focus in the present paper is on new copula-based smooth Bernstein estimators for the conditional density. Our approach avoids going through separate density estimation of numerator and denominator. Our estimator is defined as a smoother of the copula-based Bernstein estimator of the conditional distribution function. We establish asymptotic properties of bias and variance and discuss the asymptotic mean squared error in terms of the smoothing parameters. We also obtain the asymptotic normality of the new estimator. In a simulation study we show the good performance of the new estimator in comparison with other estimators proposed in the literature.-
dc.description.sponsorshipThe authors thank Dr. Charl Pretorius for his important help with the simulations. They also thank the Editor, Associate Editor and a referee for their valuable comments and suggestions. The work was supported by the IAP Research Network P7/13 of the Belgian State (Belgian Science Policy). The second author thanks the National Science Foundation of South Africa for financial support (grant number 81038). The third author is also extraordinary professor at the North-West University, Potchefstroom, South Africa.-
dc.language.isoen-
dc.rights© 2017 Elsevier Inc. All rights reserved.-
dc.subject.otherasymptotic distribution; Bernstein estimation; copula; conditional density-
dc.titleSmooth copula-based estimation of the conditional density function with a single covariate-
dc.typeJournal Contribution-
dc.identifier.epage48-
dc.identifier.spage39-
dc.identifier.volume159-
local.bibliographicCitation.jcatA1-
dc.description.notesVeraverbeke, N (reprint author), Hasselt Univ, Ctr Stat, Agoralaan,Gebouw D, B-3590 Diepenbeek, Belgium. paul.janssen@uhasselt.be; jan.swanepoel@nwu.ac.za; noel.veraverbeke@uhasselt.be-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.identifier.doi10.1016/j.jmva.2017.04.008-
dc.identifier.isi000405976900003-
item.fulltextWith Fulltext-
item.accessRightsOpen Access-
item.contributorJANSSEN, Paul-
item.contributorSwanepoel, Jan-
item.contributorVERAVERBEKE, Noel-
item.fullcitationJANSSEN, Paul; Swanepoel, Jan & VERAVERBEKE, Noel (2017) Smooth copula-based estimation of the conditional density function with a single covariate. In: JOURNAL OF MULTIVARIATE ANALYSIS, 159, p. 39-48.-
item.validationecoom 2018-
crisitem.journal.issn0047-259X-
crisitem.journal.eissn****-****-
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