A simple deconvolving Kernel density estimator when noise is Gaussian

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dc.contributor.author	Proença, Isabel
dc.date.accessioned	2023-09-06T09:03:07Z
dc.date.available	2023-09-06T09:03:07Z
dc.date.issued	2006
dc.description.abstract	Deconvolving kernel estimators when noise is Gaussian entail heavy calculations. In order to obtain the density estimates numerical evaluation of a specific integral is needed. This work proposes an approximation to the deconvolving kernel which simplifies considerably calculations by avoiding the typical numerical integration. Simulations included indicate that the lost in performance relatively to the true deconvolving kernel, is almost negligible in finite samples	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	Proença, Isabel .(2006). “A simple deconvolving Kernel density estimator when noise is Gaussian” in The Art of Semiparametrics , Contributions to Statistics Series, Physica-Verlag. S. Sperlich, W. Härdle and G. Aydinli (eds). Physica-Verlag: pp. 22-31 . (Search in 2023).	pt_PT
dc.identifier.isbn	978-3-7908-1700
dc.identifier.uri	http://hdl.handle.net/10400.5/28427
dc.language.iso	eng	pt_PT
dc.publisher	Springer	pt_PT
dc.subject	Deconvolution	pt_PT
dc.subject	Density Estimation	pt_PT
dc.subject	Errors-In-Variables	pt_PT
dc.subject	Kernel	pt_PT
dc.subject	Simulations	pt_PT
dc.title	A simple deconvolving Kernel density estimator when noise is Gaussian	pt_PT
dc.type	book part
dspace.entity.type	Publication
rcaap.rights	closedAccess	pt_PT
rcaap.type	bookPart	pt_PT

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