Single-index quantile regression models for censored data

When the dimension of the covariate space is high, semiparametric regression models become indispensable to gain flexibility while avoiding the curse of dimensionality. These considerations become even more important for incomplete data. In this work, we consider the estimation of a semiparametric single-index model for conditional quantiles with rightcensored data. Iteratively applying the local-linear smoothing approach, we simultaneously estimate the linear coefficients and the link function. We show that our estimating procedure is consistent and we study its asymptotic distribution. Numerical results are used to show the validity of our procedure and to illustrate the finite-sample performance of the proposed estimators