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Significance testing in nonparametric regression base on the bootstrap

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorDelgado, Miguel A.
dc.contributor.authorGonzález-Manteiga, Wenceslao
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned2010-01-04T12:44:44Z
dc.date.available2010-01-04T12:44:44Z
dc.date.issued1998-10
dc.description.abstractWe propose a test for selecting explanatory variables in nonparametric regression. The test does not need to estimate the conditional expectation function given all the variables but only those which are significant under the null hypothesis. This feature is compntationally convenient and solves, in part, the problem of the "curse of dimensionality" when selecting regressors in a nonparametric context. The proposed test statistic is based on functionals of an empirical process marked by nonparametric residuals. Contiguous alternatives, converging to the null at a rate n-1I2 can be detected. The asymptotic null distribution of the statistic depends on certain features of the data generating process, and asymptotic tests are difficult to implement except in rare circumstances. We justify the consistency of two bootstrap tests easy to implement, which exhibit good level accuracy for fairly small samples, according to the Monte Carlo simulations reported. These results are also applicable to test other interesting restrictions on nonparametric regression curves, like partial linearity and conditional independence.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10016/6264
dc.language.isoeng
dc.relation.ispartofseriesUC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries98-68-31
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaEstadística
dc.subject.otherNonparametric regression
dc.subject.otherseleccion of variable
dc.subject.otherhigher order kermels
dc.subject.othermarked empirical processes
dc.subject.otherWild bootstrap
dc.subject.otherrestrictions on nonparametric curves
dc.titleSignificance testing in nonparametric regression base on the bootstrap
dc.typeworking paper*
dspace.entity.typePublication
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