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Journal Articles Opuscula Mathematica Year : 2022

Strong consistency of the local linear relative regression estimator for censored data

Abstract

In this paper, we combine the local linear approach to the relative error regression estimation method to build a new estimator of the regression operator when the response variable is subject to random right censoring. We establish the uniform almost sure consistency with rate over a compact set of the proposed estimator. Numerical studies, firstly on simulated data, then on a real data set concerning the death times of kidney transplant patients, were conducted. These practical studies clearly show the superiority of the new estimator compared to competitive estimators.
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hal-04017722 , version 1 (07-03-2023)

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Feriel Bouhadjera, Elias Ould Said. Strong consistency of the local linear relative regression estimator for censored data. Opuscula Mathematica, 2022, 42 (6), pp.805-832. ⟨10.7494/opmath.2022.42.6.805⟩. ⟨hal-04017722⟩
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