Lower bounds for the trade-off between bias and mean absolute deviation

Abstract

In nonparametric statistics, rate-optimal estimators typically balance bias and stochastic error. The recent work on overparametrization raises the question whether rate-optimal estimators exist that do not obey this trade-off. In this work we consider pointwise estimation in the Gaussian white noise model with regression function f in a class of β-Hölder smooth functions. Let ’worst-case’ refer to the supremum over all functions f in the Hölder class. It is shown that any estimator with worst-case bias ≲n^{−β/(2β+1)}≕ψ_n must necessarily also have a worst-case mean absolute deviation that is lower bounded by ≳ψ_n. To derive the result, we establish abstract inequalities relating the change of expectation for two probability measures to the mean absolute deviation.