Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/11222
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dc.contributor.authorAERTS, Marc-
dc.contributor.authorHENS, Niel-
dc.contributor.authorSimonoff, Jeffrey S.-
dc.date.accessioned2010-10-05T11:07:34Z-
dc.date.availableNO_RESTRICTION-
dc.date.available2010-10-05T11:07:34Z-
dc.date.issued2010-
dc.identifier.citationJOURNAL OF APPLIED STATISTICS, 37 (9). p. 1455-1472-
dc.identifier.issn0266-4763-
dc.identifier.urihttp://hdl.handle.net/1942/11222-
dc.description.abstractIn this paper, we investigate the effect of pre-smoothing on model selection. Christobal et al 6 showed the beneficial effect of pre-smoothing on estimating the parameters in a linear regression model. Here, in a regression setting, we show that smoothing the response data prior to model selection by Akaike's information criterion can lead to an improved selection procedure. The bootstrap is used to control the magnitude of the random error structure in the smoothed data. The effect of pre-smoothing on model selection is shown in simulations. The method is illustrated in a variety of settings, including the selection of the best fractional polynomial in a generalized linear model.-
dc.description.sponsorshipWe also gratefully acknowledge the support from the IAP research network nr P5/24 of the Belgian Government (Belgian Science Policy). The research of Niel Hens has been financially supported by the Fund of Scientific Research (FWO, Research Grant # G039304) of Flanders, Belgium.-
dc.language.isoen-
dc.publisherROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD-
dc.rights2010 Taylor & Francis-
dc.subject.otherAkaike information criterion-
dc.subject.otherfractional polynomial-
dc.subject.otherlatent variable modelmodel selection-
dc.subject.otherpre-smoothing-
dc.titleModel selection in regression based on pre-smoothing-
dc.typeJournal Contribution-
dc.identifier.epage1472-
dc.identifier.issue9-
dc.identifier.spage1455-
dc.identifier.volume37-
local.format.pages18-
local.bibliographicCitation.jcatA1-
dc.description.notes[Aerts, Marc; Hens, Niel] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, B-3590 Diepenbeek, Belgium. [Simonoff, Jeffrey S.] NYU, Leonard N Stern Sch Business, New York, NY 10012 USA. marc.aerts@uhasselt.be-
local.publisher.place2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.bibliographicCitation.oldjcatA1-
dc.identifier.doi10.1080/02664760903046086-
dc.identifier.isi000281652200003-
dc.identifier.eissn1360-0532-
local.uhasselt.internationalyes-
item.accessRightsOpen Access-
item.fullcitationAERTS, Marc; HENS, Niel & Simonoff, Jeffrey S. (2010) Model selection in regression based on pre-smoothing. In: JOURNAL OF APPLIED STATISTICS, 37 (9). p. 1455-1472.-
item.contributorAERTS, Marc-
item.contributorHENS, Niel-
item.contributorSimonoff, Jeffrey S.-
item.fulltextWith Fulltext-
item.validationecoom 2011-
crisitem.journal.issn0266-4763-
crisitem.journal.eissn1360-0532-
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