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

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

Feriel Bouhadjera (ULCO)

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
over a compact set and its asymptotic normality. The asymptotic variance is explicitly given and as product we give a confidence bands. A simulation study has been conducted to comfort our theoretical results.

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