Moments, errors, asymptotic normality and large deviation principle in nonparametric functional regression

Recently, some nonparametric regression ideas have been extended to the functional context, allowing infinite-dimensional regressors. This paper gives a deep asymptotic study of the functional Nadaraya–Watson estimator, including moments of all orders, errors, asymptotic distribution and large deviation rate.