On the super-efficiency and robustness of the least squares of depth-trimmed regression estimator

TL;DR

This paper investigates the super-efficiency and robustness of the least squares of depth-trimmed (LST) residuals regression, showing it can outperform traditional robust estimators in efficiency and robustness, with improved computational speed.

Contribution

It reveals the super-efficiency of LST regression, compares its robustness and efficiency to LTS and MM estimators, and introduces a faster algorithm for its computation.

Findings

LST can be as efficient as or more efficient than LS under certain conditions.

LST outperforms LTS in robustness and the MM estimator in efficiency and robustness.

The new algorithm enables faster computation of LST regression.

Abstract

The least squares of depth-trimmed (LST) residuals regression, proposed and studied in Zuo and Zuo (2023), serves as a robust alternative to the classic least squares (LS) regression as well as a strong competitor to the renowned robust least trimmed squares (LTS) regression of Rousseeuw (1984). The aim of this article is three-fold. (i) to reveal the super-efficiency of the LST and demonstrate it can be as efficient as (or even more efficient than) the LS in the scenarios with errors uncorrelated and mean zero and homoscedastic with finite variance and to explain this anti-Gaussian-Markov-Theorem phenomenon; (ii) to demonstrate that the LST can outperform the LTS, the benchmark of robust regression estimator, on robustness, and the MM of Yohai (1987), the benchmark of efficient and robust estimator, on both efficiency and robustness, consequently, could serve as an alternative to both; (iii) to promote the implementation and computation of the LST regression for a broad group of statisticians in statistical practice and to demonstrate that it can be computed as fast as (or even faster than) the LTS based on a newly improved algorithm.