Testing strict monotonicity in nonparametric regression

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DC Field	Value	Language
dc.contributor.author	Birke, Melanie	-
dc.contributor.author	Dette, Holger	-
dc.date.accessioned	2007-02-21T14:44:43Z	-
dc.date.available	2007-02-21T14:44:43Z	-
dc.date.issued	2007-02-21T14:44:43Z	-
dc.identifier.uri	http://hdl.handle.net/2003/23300	-
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-27	-
dc.description.abstract	A new test for strict monotonicity of the regression function is proposed which is based on a composition of an estimate of the inverse of the regression function with a common regression estimate. This composition is equal to the identity if and only if the “true” regression function is strictly monotone, and a test based on an L2-distance is investigated. The asymptotic normality of the corresponding test statistic is established under the null hypothesis of strict monotonicity. AMS Subject Classification: 62G10	en
dc.language.iso	en	de
dc.subject	Goodness-of-fit test	en
dc.subject	Nonparametric regression	en
dc.subject	Strictly monotone regression	en
dc.subject.ddc	004	-
dc.title	Testing strict monotonicity in nonparametric regression	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access	-
Appears in Collections:	Sonderforschungsbereich (SFB) 475

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