Asymptotic Properties of Kernel Estimators Based on Local Medians

The desire to make nonparametric regression robust leads to the problem of conditional median function estimation. Under appropriate regularity conditions, a sequence of local median estimators can be chosen to achieve the optimal rate of convergence n-1/(2+d)both pointwise and in the$L^q (1 \leq q < \infty)$norm restricted to a compact. It can also be chosen to achieve the optimal rate of convergence (n-1log n)1/(2+d)in the L∞norm restricted to a compact. These results also constitute an answer to an open question of Stone.