A comparative study of monotone nonparametric kernel estimates

Dette, Holger; Pilz, Kay F.

Full metadata record

DC Field	Value	Language
dc.contributor.author	Dette, Holger	de
dc.contributor.author	Pilz, Kay F.	de
dc.date.accessioned	2004-12-06T18:39:09Z	-
dc.date.available	2004-12-06T18:39:09Z	-
dc.date.issued	2004	de
dc.identifier.uri	http://hdl.handle.net/2003/4892	-
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-6924	-
dc.format.extent	198424 bytes	-
dc.format.extent	301258 bytes	-
dc.format.extent	448842 bytes	-
dc.format.mimetype	application/pdf	-
dc.format.mimetype	application/pdf	-
dc.format.mimetype	application/postscript	-
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject	In this paper we present a detailed numerical comparison of three monotone nonparametric kernel regression estimates, which isotonize a nonparametric curve estimator. The first estimate is the classical smoothed isotone estimate of Brunk (1958). The second method has recently been proposed by Hall and Huang (2001) and modifies the weights of a commonly used kernel estimate such that the resulting estimate is monotone. The third estimate was recently proposed by Dette, Neumeyer and Pilz (2003) and combines density and regression estimation techniques to obtain a monotone curve estimate of the inverse of the isotone regression function. The three concepts are briefly reviewed and their finite sample properties are studied by means of a simulation study. Although all estimates are first order asymptotically equivalent (provided that the unknown regression function is isotone) some differences for moderate samples are observed.	en
dc.subject	isotonic regression	en
dc.subject	order restricted inference	en
dc.subject	Nadaraya-Watson estimator	en
dc.subject	local linear regression	en
dc.subject	monte carlo simulation	en
dc.subject.ddc	310	de
dc.title	A comparative study of monotone nonparametric kernel estimates	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access	-
Appears in Collections:	Sonderforschungsbereich (SFB) 475

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