Asymptotic normality of kernel functional regression estimator based upon twice censored data

In this paper, we introduce a new kernel functional regression estimator when the random response variable is subject to twice censoring. Then, we establish the asymptotic normality of our estimator and we deduce the asymptotic confidence interval of the regression function. To enhance our theoretical results, the performance and the asymptotic Gaussian behavior of our estimator are highlighted through a simulation study and an application to real data.