Testing monotonicity of regression functions - an empirical process approach

dc.contributor.authorBirke, Melanie
dc.contributor.authorNeumeyer, Natalie
dc.date.accessioned2010-04-12T08:41:58Z
dc.date.available2010-04-12T08:41:58Z
dc.date.issued2010-04-12T08:41:58Z
dc.description.abstractWe propose several new tests for monotonicity of regression functions based on different empirical processes of residuals. The residuals are obtained from recently developed simple kernel based estimators for increasing regression functions based on increasing rearrangements of unconstrained nonparametric estimators. The test statistics are estimated distance measures between the regression function and its increasing rearrangement. We discuss the asymptotic distributions, consistency, and small sample performances of the tests. AMS Classification: 62G10, 62G08, 62G30en
dc.identifier.urihttp://hdl.handle.net/2003/27109
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-48
dc.language.isoen
dc.relation.ispartofseriesDiscussion Paper / SFB 823;09/2010
dc.subjectKolmogorov-Smirnov testen
dc.subjectModel testen
dc.subjectMonotone rearrangementen
dc.subjectNonparametric regressionen
dc.subjectResidual processen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleTesting monotonicity of regression functions - an empirical process approachen
dc.typeText
dc.type.publicationtypereport
dcterms.accessRightsopen access
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