Testing for Symmetric Error Distribution in Nonparametric Regression Models

dc.contributor.author	Dette, Holger	de
dc.contributor.author	Neumeyer, Natalie	de
dc.date.accessioned	2004-12-06T18:40:53Z
dc.date.available	2004-12-06T18:40:53Z
dc.date.issued	2003	de
dc.description.abstract	For the problem of testing symmetry of the error distribution in a nonparametric regression model we propose as a test statistic the difference between the two empirical distribution functions of estimated residuals and their counterparts with opposite signs. The weak convergence of the difference process to a Gaussian process is shown. The covariance structure of this process depends heavily on the density of the error distribution, and for this reason the performance of a symmetric wild bootstrap procedure is discussed in asymptotic theory and by means of a simulation study. In contrast to the available procedures the new test is also applicable under heteroscedasticity.	en
dc.format.extent	183414 bytes
dc.format.extent	374065 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.uri	http://hdl.handle.net/2003/4973
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15099
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject	empirical process of residuals	en
dc.subject	testing for symmetry	en
dc.subject	nonparametric regression	en
dc.subject.ddc	310	de
dc.title	Testing for Symmetric Error Distribution in Nonparametric Regression Models	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access

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