Chown, JustinMüller, Ursula U.2016-10-142016-10-142016http://hdl.handle.net/2003/3528410.17877/DE290R-17327Heteroskedastic 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.enDiscussion Paper / SFB823;50, 2016heteroskedastic nonparametric regressionweighted empirical processtransfer principlemissing at randomlocal polynomial smoother310330620working paperHeteroskedastizitätNichtparametrische RegressionStatistischer Test