Asymptotic normality of robust $M$-estimators with convex penalty

TL;DR

This paper establishes asymptotic normality for individual coordinates of high-dimensional robust M-estimators with convex penalties, providing data-driven variance estimates that adapt to different dimensional regimes.

Contribution

It introduces a bias correction for asymptotic normality of M-estimators with convex penalties in high dimensions, including the Huber loss, and develops data-driven variance estimation methods.

Findings

Asymptotic normality holds for most coordinates with bias correction.

Variance estimates adapt to low and high-dimensional regimes.

Simulation confirms theoretical results.

Abstract

This paper develops asymptotic normality results for individual coordinates of robust M-estimators with convex penalty in high-dimensions, where the dimension $p$ is at most of the same order as the sample size $n$, i.e, $p/n\le\gamma$ for some fixed constant $\gamma>0$. The asymptotic normality requires a bias correction and holds for most coordinates of the M-estimator for a large class of loss functions including the Huber loss and its smoothed versions regularized with a strongly convex penalty. The asymptotic variance that characterizes the width of the resulting confidence intervals is estimated with data-driven quantities. This estimate of the variance adapts automatically to low ($p/n\to0)$ or high ($p/n \le \gamma$) dimensions and does not involve the proximal operators seen in previous works on asymptotic normality of M-estimators. For the Huber loss, the estimated variance has a simple expression involving an effective degrees-of-freedom as well as an effective sample size. The case of the Huber loss with Elastic-Net penalty is studied in details and a simulation study confirms the theoretical findings. The asymptotic normality results follow from Stein formulae for high-dimensional random vectors on the sphere developed in the paper which are of independent interest.