2008/03/07 by Lopez, Olivier
#62G08 #62G20 #62N01 #62N02 #FOS: Mathematics #Statistics Theory (math.ST)
paper · doi:10.48550/arxiv.0803.1112
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.