Uniform convergence rates for nonparametric estimators smoothed by the beta kernel

摘要

This paper provides a set of uniform consistency results with rates for nonparametric density and regression estimators smoothed by the beta kernel having support on the unit interval. Weak and strong uniform convergence is explored on the basis of expanding compact sets and general sequences of smoothing parameters. The results in this paper are useful for asymptotic analysis of two-step semiparametric estimation using a first-step kernel estimate as a plug-in. We provide simulations and a real data example illustrating attractive properties of the estimators.