Orthogonal Subsampling for Big Data Linear Regression

TL;DR

This paper introduces an orthogonal subsampling method for big data linear regression that improves estimation accuracy, is computationally efficient, and suitable for distributed systems, outperforming existing subsampling techniques.

Contribution

The paper proposes a novel orthogonal subsampling approach for big data linear regression, enhancing efficiency, robustness, and suitability for parallel computing.

Findings

OSS outperforms existing methods in minimizing mean squared errors.

OSS provides more precise estimates of interaction effects.

The approach is robust to covariate interactions.

Abstract

The dramatic growth of big datasets presents a new challenge to data storage and analysis. Data reduction, or subsampling, that extracts useful information from datasets is a crucial step in big data analysis. We propose an orthogonal subsampling (OSS) approach for big data with a focus on linear regression models. The approach is inspired by the fact that an orthogonal array of two levels provides the best experimental design for linear regression models in the sense that it minimizes the average variance of the estimated parameters and provides the best predictions. The merits of OSS are three-fold: (i) it is easy to implement and fast; (ii) it is suitable for distributed parallel computing and ensures the subsamples selected in different batches have no common data points; and (iii) it outperforms existing methods in minimizing the mean squared errors of the estimated parameters and maximizing the efficiencies of the selected subsamples. Theoretical results and extensive numerical results show that the OSS approach is superior to existing subsampling approaches. It is also more robust to the presence of interactions among covariates and, when they do exist, OSS provides more precise estimates of the interaction effects than existing methods. The advantages of OSS are also illustrated through analysis of real data.