A new class of robust regression estimators is proposed that forms an alternative to traditional robust one-step estimators and that achieves the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$\sqrt{n}$</EquationSource> </InlineEquation> rate of convergence irrespective of the initial estimator under a wide range of distributional assumptions. The proposed reweighted least...</equationsource></inlineequation>

Quantile regression in the presence of fixed censoring has been studied extensively in the literature. However, existing methods either suffer from computational instability or require complex procedures involving trimming and smoothing, which complicates the asymptotic theory of the resulting...