Single-index Regression models with right-censored responses

Olivier Lopez (1, 2)

1 IRMAR - Institut de Recherche Mathématique de Rennes
2 CREST - Centre de Recherche en Économie et Statistique

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.

Mots clés: single-index models; semiparametric regression; dimension reduction; censored regression; Kaplan-Meier estimator; single-index models.

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Olivier Lopez. Single-index Regression models with right-censored responses. Journal of Statistical Planning and Inference, 2009, 139 (3), pp.1082-1097. ⟨10.1016/j.jspi.2008.06.012⟩. ⟨hal-00261412⟩