Global Conference on Mathematics and Applications (GCMA)

This work proposes a kernel-based local linear estimator for functional regression in the
presence of missing responses, under the assumption of missing at random . Our primary theoretical result is to establish the asymptotic normality of the proposed estimator. The finite sample performance of our approach is evaluated through an extensive simulation study, which
confirms its practical effectiveness