DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Heeyoung | ko |
dc.contributor.author | Huo, Xiaoming | ko |
dc.date.accessioned | 2014-11-25T09:32:23Z | - |
dc.date.available | 2014-11-25T09:32:23Z | - |
dc.date.created | 2013-12-24 | - |
dc.date.created | 2013-12-24 | - |
dc.date.created | 2013-12-24 | - |
dc.date.issued | 2012 | - |
dc.identifier.citation | JOURNAL OF NONPARAMETRIC STATISTICS, v.24, no.3, pp.665 - 680 | - |
dc.identifier.issn | 1048-5252 | - |
dc.identifier.uri | http://hdl.handle.net/10203/191186 | - |
dc.description.abstract | Smoothing splines are widely used for estimating an unknown function in the nonparametric regression. If data have large spatial variations, however, the standard smoothing splines (which adopt a global smoothing parameter lambda) perform poorly. Adaptive smoothing splines adopt a variable smoothing parameter lambda(x) (i.e. the smoothing parameter is a function of the design variable x) to adapt to varying roughness. In this paper, we derive an asymptotically optimal local penalty function for lambda(x) is an element of C-3 under suitable conditions. The derived locally optimal penalty function in turn is used for the development of a locally optimal adaptive smoothing spline estimator. In the numerical study, we show that our estimator performs very well using several simulated and real data sets. | - |
dc.language | English | - |
dc.publisher | TAYLOR FRANCIS LTD | - |
dc.subject | BAYESIAN CONFIDENCE-INTERVALS | - |
dc.subject | NONPARAMETRIC REGRESSION | - |
dc.subject | CROSS-VALIDATION | - |
dc.subject | NOISY DATA | - |
dc.subject | KERNEL | - |
dc.title | Locally optimal adaptive smoothing splines | - |
dc.type | Article | - |
dc.identifier.wosid | 000307936400008 | - |
dc.identifier.scopusid | 2-s2.0-84865260946 | - |
dc.type.rims | ART | - |
dc.citation.volume | 24 | - |
dc.citation.issue | 3 | - |
dc.citation.beginningpage | 665 | - |
dc.citation.endingpage | 680 | - |
dc.citation.publicationname | JOURNAL OF NONPARAMETRIC STATISTICS | - |
dc.identifier.doi | 10.1080/10485252.2012.693610 | - |
dc.contributor.localauthor | Kim, Heeyoung | - |
dc.contributor.nonIdAuthor | Huo, Xiaoming | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | adaptive smoothing splines | - |
dc.subject.keywordAuthor | kernel smoothing | - |
dc.subject.keywordAuthor | optimal bandwidth | - |
dc.subject.keywordAuthor | optimal penalty function | - |
dc.subject.keywordPlus | BAYESIAN CONFIDENCE-INTERVALS | - |
dc.subject.keywordPlus | NONPARAMETRIC REGRESSION | - |
dc.subject.keywordPlus | CROSS-VALIDATION | - |
dc.subject.keywordPlus | NOISY DATA | - |
dc.subject.keywordPlus | KERNEL | - |
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