A martingale-transform goodness-of-fit test for the form of the conditional variance

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dc.creatorDette, H.
dc.creatorHetzler, B.
dc.date2008-09-29
dc.date.accessioned2026-07-07T10:06:07Z
dc.date.available2026-07-07T10:06:07Z
dc.descriptionIn the common nonparametric regression model the problem of testing for a specific parametric form of the variance function is considered. Recently Dette and Hetzler (2008) proposed a test statistic, which is based on an empirical process of pseudo residuals. The process converges weakly to a Gaussian process with a complicated covariance kernel depending on the data generating process. In the present paper we consider a standardized version of this process and propose a martingale transform to obtain asymptotically distribution free tests for the corresponding Kolmogorov-Smirnov and Cramér-von-Mises functionals. The finite sample properties of the proposed tests are investigated by means of a simulation study.
dc.description24 pages,
dc.identifierhttps://arxiv.org/abs/0809.4914
dc.identifierhttp://arxiv.org/abs/0809.4914
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/170218
dc.subjectStatistics Theory
dc.subject62G05
dc.titleA martingale-transform goodness-of-fit test for the form of the conditional variance
dc.typetext

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