Mean square convergence of functional conditional distribution estimators under twice censoring

In this work, we propose a novel kernel estimator for the conditional distribution in the twice censored model, with functional regressors. We next analyze its mean square convergence, with an explicit rate given in Theorem [t1]. To validate our theoretical result, we conduct a simulation study that demonstrates the estimator's accuracy and performance, further supporting the robustness of our method, even with twice censored data.