Benseradj, H.Guessoum, Z.2021-01-172021-01-17202003610926DOI: 10.1080/03610926.2020.1764037https://www.scopus.com/record/display.uri?eid=2-s2.0-85085030721&origin=SingleRecordEmailAlert&dgcid=raven_sc_affil_en_us_email&txGid=7da73033bd71e8f54e5c3658f3b7e486https://dspace.univ-boumerdes.dz/handle/123456789/6145In this paper, we propose a non parametric M-estimator of the regression function and we investigate its asymptotic properties, when the response variable is subject to both random left truncation and right censoring. In most works, non parametric M-estimation requires the use of an objective function ψ supposed to be bounded. Here the results hold with unbounded objective function. The strong uniform consistency rate is established under α-mixing dependence. A large simulation study with one and bi-dimensional regressor is conducted for fixed and local bandwidths to highlight the good behavior of our estimatorenAlpha-mixingM-estimatorRobust regressionStrong uniform consistency rateArticle