Variance estimation in nonparametric regression via the difference sequence method

The Annals of Statistics · 2007 · Volume 35 · Issue 5

Abstract

Consider a Gaussian nonparametric regression problem having both an unknown mean function and unknown variance function. This article presents a class of difference-based kernel estimators for the variance function. Optimal convergence rates that are uniform over broad functional classes and bandwidths are fully characterized, and asymptotic normality is also established. We also show that for suitable asymptotic formulations our estimators achieve the minimax rate.

Keywords

62G08, 62G20, asymptotic minimaxity, Nonparametric regression, variance estimation

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