A Note on Testing Symmetry of the Error Distribution in Linear Regression Models

dc.contributor.author	Dette, Holger	de
dc.contributor.author	Nagel, Eva-Renate	de
dc.contributor.author	Neumeyer, Natalie	de
dc.date.accessioned	2004-12-06T18:41:14Z
dc.date.available	2004-12-06T18:41:14Z
dc.date.issued	2003	de
dc.description.abstract	In the classical linear regression model the problem of testing for symmetry of the error distribution is considered. The test statistic is a functional of the difference between the two empirical distribution functions of the estimated residuals and their counterparts with opposite signs. The weak convergence of the difference process to a Gaussian process is established. 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.	en
dc.format.extent	192577 bytes
dc.format.extent	411687 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.uri	http://hdl.handle.net/2003/4987
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15048
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject	M-estimation	en
dc.subject	goodness-of-fit tests	en
dc.subject	testing for symmetry	en
dc.subject	empirical process of residuals	en
dc.subject	linear model	en
dc.subject.ddc	310	de
dc.title	A Note on Testing Symmetry of the Error Distribution in Linear Regression Models	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access

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