Regularization parameter selection in indirect regression by residual based bootstrap - Bissantz, Nicolai; Chown, Justin; Dette, Holger (Technische Universität Dortmund, 2016)
Residual-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.
Discussion Paper / SFB823;56, 2016
Keywords: bandwidth selection; smooth bootstrap; residual-based empirical distribution function; regularization; indirect nonparametric regression; deconvolution function estimator; 310; 330; 620; Nichtparametrische Regression; Bootstrap-Statistik