Single-index Regression models with right-censored responses
Statistics Theory
2008-12-18 v1 Statistics Theory
Abstract
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.
Cite
@article{arxiv.0803.1112,
title = {Single-index Regression models with right-censored responses},
author = {Olivier Lopez},
journal= {arXiv preprint arXiv:0803.1112},
year = {2008}
}