Averaging estimators for kernel regressions

This paper considers model averaging for kernel regressions. We construct a weighted average of the local constant and local linear estimators at each point of estimation. We propose a two-step cross-validation method for bandwidths and weights selection, and derive the rate of convergence of the cross-validation weights to their optimal benchmark values.