Bissantz, NicolaiChown, JustinDette, Holger2016-10-282016-10-282016http://hdl.handle.net/2003/3530210.17877/DE290R-17345Residual-based analysis is generally considered a cornerstone of statistical methodology. For a special case of indirect regression, we investigate the residual-based empirical distribution function and provide a uniform expansion of this estimator, which is also shown to be asymptotically most precise. This investigation naturally leads to a completely data-driven technique for selecting a regularization parameter used in our indirect regression function estimator. The resulting methodology is based on a smooth bootstrap of the model residuals. A simulation study demonstrates the effectiveness of our approach.enDiscussion Paper / SFB823;56, 2016bandwidth selectionsmooth bootstrapresidual-based empirical distribution functionregularizationindirect nonparametric regressiondeconvolution function estimator310330620working paperNichtparametrische RegressionBootstrap-Statistik