Detecting heteroskedasticity in nonparametric regression using weighted empirical processes

dc.contributor.authorChown, Justin
dc.contributor.authorMüller, Ursula U.
dc.date.accessioned2016-10-14T10:19:04Z
dc.date.available2016-10-14T10:19:04Z
dc.date.issued2016
dc.description.abstractHeteroskedastic errors can lead to inaccurate statistical conclusions if they are not properly handled. We introduce a test for heteroskedasticity for the nonparametric regression model with multiple covariates. It is based on a suitable residual-based empirical distribution function. The residuals are constructed using local polynomial smoothing. Our test statistic involves a "detection function" that can verify heteroskedasticity by exploiting just the independence-dependence structure between the detection function and model errors, i.e. we do not require a specific model of the variance function. The procedure is asymptotically distribution free: inferences made from it do not depend on unknown parameters. It is consistent at the parametric (root-n) rate of convergence. Our results are extended to the case of missing responses and illustrated with simulations.en
dc.identifier.urihttp://hdl.handle.net/2003/35284
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-17327
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;50, 2016en
dc.subjectheteroskedastic nonparametric regressionen
dc.subjectweighted empirical processen
dc.subjecttransfer principleen
dc.subjectmissing at randomen
dc.subjectlocal polynomial smootheren
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.subject.rswkHeteroskedastizitätde
dc.subject.rswkNichtparametrische Regressionde
dc.subject.rswkStatistischer Testde
dc.titleDetecting heteroskedasticity in nonparametric regression using weighted empirical processesen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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