Single index regression models in the presence of censoring depending on the covariates

摘要

Consider a random vector (X ′,Y) ′, where X is d-dimensional and Y is one-dimensional.We assume that Y is subject to random right censoring. The aim of this paper is twofold. First, we propose a new estimator of the joint distribution of (X ′,Y) ′. This estimator overcomes the common curse-of-dimensionality problem, by using a new dimension reduction technique. Second, we assume that the relation between X and Y is given by a mean regression single index model, and propose a new estimator of the parameters in this model. The asymptotic properties of all proposed estimators are obtained.