L.M.P.A
Laboratoire de Mathématiques Pures et Appliquées
Joseph Liouville

Groupe de travail de Probabilités, statistiques, théorie ergodique du 21 mars 2019

Let $(T_i)_i$ be a sequence of independent identically distributed (i.i.d.) random variables (r.v.) of interest distributed as $T$ and $(X_i)_i$ be a corresponding vector of covariates taking values on $\mathbb{R}^d$. In
censorship models the r.v. $T$ is subject to random censoring by another r.v. $C$. In this contribution we built a new kernel estimator based on the so-called synthetic data of the mean squared relative error for the regression function. We establish the uniform almost sure convergence with rate