Asymptotic properties for an M-estimator of the regression function with truncation and dependent data

In this paper, we construct a nonparametric M-estimator of a regression function for a left truncated model when the data exhibit some kind of dependence. It is assumed that the observations form a stationary αα-mixing sequence. Under appropriate assumptions, we establish weak and strong consistency of the estimator as well as its asymptotic normality. Finite sample behavior of the estimators shows that the M-estimator is more robust than the Nadaraya–Watson type estimator.