Kernel estimation for additive models under dependence

AbstractNonparametric estimation of the conditional mean function for additive models is investigated in cases where the observed data are dependent. We use an additive kernel estimator which is a sum of Nadaraya—Watson estimators. Under a strong mixing condition, the kernel estimator is shown to be asymptotically normal and to achieve the univariate optimal rate of convergence in mean squared error