Inference robust to outliers with L1‐norm penalization

Abstract

This paper considers the problem of inference in a linear regression model with outliers where the number of outliers can grow with sample size but their proportion goes to 0. We apply an estimator penalizing the `1-norm of a random vector which is non-zero for outliers. We derive rates of convergence and asymptotic normality. Our estimator has the same asymptotic variance as the OLS estimator in the standard linear model. This enables to build tests and confidence sets in the usual and simple manner. The proposed procedure is also computationally advantageous as it amounts to solving a convex optimization program. Overall, the suggested approach constitutes a practical robust alternative to the ordinary least squares estimator.

Keywords

Replaced by

Jad Beyhum, “Inference robust to outliers with L1‐norm penalization”, ESAIM: Probability and Statistics, vol. 24, November 2020, pp. 688–702.

Reference

Jad Beyhum, “Inference robust to outliers with L1‐norm penalization”, TSE Working Paper, n. 19-1032, August 2019.