A Fast Algorithm for Robust Regression with Penalised Trimmed Squares

TL;DR

This paper introduces an efficient approximate algorithm called Fast-PTS for robust linear regression using the Penalised Trimmed Squares estimator, which effectively detects high leverage outliers even in large datasets.

Contribution

The paper extends the theoretical understanding of the PTS estimator and proposes a fast algorithm to compute it efficiently for large-scale problems.

Findings

Fast-PTS accurately identifies high leverage outliers.

The algorithm performs well on benchmark datasets with various outlier levels.

Fast-PTS is computationally feasible for large data sets.

Abstract

The presence of groups containing high leverage outliers makes linear regression a difficult problem due to the masking effect. The available high breakdown estimators based on Least Trimmed Squares often do not succeed in detecting masked high leverage outliers in finite samples. An alternative to the LTS estimator, called Penalised Trimmed Squares (PTS) estimator, was introduced by the authors in \cite{ZiouAv:05,ZiAvPi:07} and it appears to be less sensitive to the masking problem. This estimator is defined by a Quadratic Mixed Integer Programming (QMIP) problem, where in the objective function a penalty cost for each observation is included which serves as an upper bound on the residual error for any feasible regression line. Since the PTS does not require presetting the number of outliers to delete from the data set, it has better efficiency with respect to other estimators. However, due to the high computational complexity of the resulting QMIP problem, exact solutions for moderately large regression problems is infeasible. In this paper we further establish the theoretical properties of the PTS estimator, such as high breakdown and efficiency, and propose an approximate algorithm called Fast-PTS to compute the PTS estimator for large data sets efficiently. Extensive computational experiments on sets of benchmark instances with varying degrees of outlier contamination, indicate that the proposed algorithm performs well in identifying groups of high leverage outliers in reasonable computational time.