A sub-sampling algorithm preventing outliers

TL;DR

This paper introduces a novel subsampling algorithm that avoids outliers and high leverage points, improving the robustness of linear model estimation from large datasets by using D- and I-optimality criteria.

Contribution

It proposes both unsupervised and supervised exchange procedures for subsampling that prevent outliers and leverage points, enhancing model accuracy and robustness.

Findings

The algorithms effectively reduce the influence of outliers and leverage points.

The methods improve the accuracy of linear models on large datasets.

Both procedures are generalized to I-optimality for better prediction accuracy.

Abstract

Nowadays, in many different fields, massive data are available and for several reasons, it might be convenient to analyze just a subset of the data. The application of the D-optimality criterion can be helpful to optimally select a subsample of observations. However, it is well known that D-optimal support points lie on the boundary of the design space and if they go hand in hand with extreme response values, they can have a severe influence on the estimated linear model (leverage points with high influence). To overcome this problem, firstly, we propose an unsupervised exchange procedure that enables us to select a nearly D-optimal subset of observations without high leverage values. Then, we provide a supervised version of this exchange procedure, where besides high leverage points also the outliers in the responses (that are not associated to high leverage points) are avoided. This is possible because, unlike other design situations, in subsampling from big datasets the response values may be available. Finally, both the unsupervised and the supervised selection procedures are generalized to I-optimality, with the goal of getting accurate predictions.