Asymptotic normality and strong consistency of kernel regression estimation in q-calculus

We construct a family of estimators for a regression function based on a sample following a qdistribution. Our approach is nonparametric, using kernel methods built from operations that leverage the properties of q-calculus. Furthermore, under appropriate assumptions, we establish the weak convergence and strong consistency of this family of estimators.