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Cross-validating fit and predictive accuracy of nonlinear quantile regressions

Haupt, Harry ; Kagerer, Kathrin ; Schnurbus, Joachim



Abstract

The paper proposes a cross-validation method to address the question of specification search in a multiple nonlinear quantile regression framework. Linear parametric, spline-based partially linear, and kernel-based fully nonparametric specifications are contrasted as competitors using cross-validated weighted L1-norm based goodness-of-fit and prediction error criteria. The aim is to provide a ...

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