  In this article, we propose some new generalizations of M-estimation
procedures for single-index regression models in presence of randomly
right-censored responses. We derive consistency and asymptotic normality of our
estimates. The results are proved in order to be adapted to a wide range of
techniques used in a censored regression framework (e.g. synthetic data or
weighted least squares). As in the uncensored case, the estimator of the
single-index parameter is seen to have the same asymptotic behavior as in a
fully parametric scheme. We compare these new estimators with those based on
the average derivative technique of Burke and Lu (2005) through a simulation
study.
