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dc.contributor.authorGiordano, Francesco
dc.contributor.authorParrella, Maria Lucia
dc.date.accessioned2019-11-08T13:51:51Z
dc.date.available2019-11-08T13:51:51Z
dc.date.issued2009
dc.identifier.citationGiordano, F. and Parrella, M. L. (2009). “A locally adaptive bandwidth selector for kernel based regression”. DISES Working Paper 3.209, Università degli Studi di Salerno, Dipartimento di Scienze Economiche e Statistiche.it_IT
dc.identifier.issn1971-3029it_IT
dc.identifier.urihttp://elea.unisa.it:8080/xmlui/handle/10556/3809
dc.identifier.urihttp://dx.doi.org/10.14273/unisa-2031
dc.description.abstractThe selection of the smoothing parameter represents a crucial step in the local polynomial regression, because of the implications on the consistency of the nonparametric regression estimator and because of the difficulties in the implementation of the selection procedure. Moreover, to capture the complexity of the unknown regression curve, a local variable bandwidth is needed, which determines an increase in the efficiency and computa- tional costs of such algorithms. This paper focuses on the problem of the automatic selection of a local bandwidth. We propose a slightly different approach with respect to the traditional ones, which does not require ad- ditional computational effort. The empirical performance of the method is shown in the paper through a simulation study.it_IT
dc.format.extent38 p.it_IT
dc.language.isoenit_IT
dc.relation.ispartofWorking Papers ; 3.209it_IT
dc.sourceUniSa. Sistema Bibliotecario di Ateneoit_IT
dc.subjectNonparametric regressionit_IT
dc.subjectVariable bandwidth selectionit_IT
dc.subjectDerivative estimationit_IT
dc.subjectNeural networksit_IT
dc.subjectLocal polynomialsit_IT
dc.subjectDependent datait_IT
dc.titleA locally adaptive bandwidth selector for kernel based regressionit_IT
dc.typeWorking Paperit_IT
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