Optimal Subsampling Design for Polynomial Regression in one Covariate

TL;DR

This paper develops D-optimal subsampling strategies for polynomial regression with a single covariate, focusing on invariant distributions, and compares their efficiency to uniform random subsampling.

Contribution

It introduces a method to construct D-optimal subsampling designs for polynomial regression in one covariate, including specific insights for quadratic regression.

Findings

Optimal designs depend on the covariate distribution shape

Subsample size influences the design structure

Optimal designs outperform uniform random subsampling

Abstract

Improvements in technology lead to increasing availability of large data sets which makes the need for data reduction and informative subsamples ever more important. In this paper we construct $ D $-optimal subsampling designs for polynomial regression in one covariate for invariant distributions of the covariate. We study quadratic regression more closely for specific distributions. In particular we make statements on the shape of the resulting optimal subsampling designs and the effect of the subsample size on the design. To illustrate the advantage of the optimal subsampling designs we examine the efficiency of uniform random subsampling.