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dc.contributor.authorCheng, Ming-Yenen
dc.contributor.authorHall, Peteren
dc.contributor.authorTurlach, Berwin A.en
dc.identifier.citationBiometrika, 1999; 86(2):417-428en
dc.description.abstractWe suggest a method for using parametric information to modify a nonparametric estimator at the level of relatively high-order derivatives. The technique represents an alternative to methods that first fit a parametric model and then adjust it. In particular, relative to a 'nonparametric estimator with a parametric start', our estimator is not biased by the differences between parametric and nonparametric fits to low-order derivatives, since we effectively remove all the parametric information about low-order derivatives and replace it by nonparametric information. Thus, we employ parametric information only when the nonparametric information. Thus, we employ parametric information only when the nonparametric information is unreliable, and do not use it elsewhere. The method has application to both nonparametric density estimation and nonparametric regression.en
dc.description.statementofresponsibilityM-Y. Cheng, P. Hall and B. A. Turlachen
dc.rights© 1999 Biometrika Trusten
dc.subjectBias reduction ; Curve estimation ; Density estimation ; Kernel regression ; Local polynomial regression ; Locally parametric methods ; Log-polynomial model ; Nonparametric regressionen
dc.titleHigh-derivative parametric enhancements of nonparametric curve estimatorsen
dc.typeJournal articleen
Appears in Collections:Applied Mathematics publications

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