Central limit theorems for the integrated squared error of derivative estimators

A central limit theorem for the weighted integrated squared error of kernel-type estimators of the first two derivatives of a nonparametric regression function is proved by using results for martingale differences and U-statistics. The results focus on the setting of the Nadaraya-Watson estimator but can also be transferred to local polynomial estimates.