Least sum of squares of trimmed residuals regression

TL;DR

This paper introduces the LST estimator, a robust regression method that trims residuals before squaring, offering high breakdown point and efficiency, with algorithms demonstrated to be fast and effective on synthetic and real data.

Contribution

It proposes the LST estimator, a novel robust regression approach that trims residuals prior to squaring, improving robustness and computational efficiency over existing methods.

Findings

LST has a high finite sample breakdown point.

LST can resist up to 50% contamination asymptotically.

Algorithms for computing LST are fast and produce smaller variances.

Abstract

In the famous least sum of trimmed squares (LTS) of residuals estimator (Rousseeuw (1984)), residuals are first squared and then trimmed. In this article, we first trim residuals - using a depth trimming scheme - and then square the rest of residuals. The estimator that can minimize the sum of squares of the trimmed residuals, is called an LST estimator. It turns out that LST is a robust alternative to the classic least sum of squares (LS) estimator. Indeed, it has a very high finite sample breakdown point, and can resist, asymptotically, up to $50\%$ contamination without breakdown - in sharp contrast to the $0\%$ of the LS estimator. The population version of LST is Fisher consistent, and the sample version is strong and root-$n$ consistent and asymptotically normal. Approximate algorithms for computing LST are proposed and tested in synthetic and real data examples. These experiments indicate that one of the algorithms can compute the LST estimator very fast and with relatively smaller variances than the famous LTS estimator. All the evidence suggests that LST deserves to be a robust alternative to the LS estimator and is feasible in practice for high dimensional data sets (with possible contamination and outliers).