Efficient subsampling for high-dimensional data

TL;DR

This paper introduces an efficient two-step method combining variable selection via a random LASSO-inspired procedure and leverage score-based subdata sampling to improve predictive accuracy and computational efficiency in high-dimensional big data analysis.

Contribution

The paper presents a novel approach for high-dimensional data that effectively combines variable selection and subdata sampling, outperforming existing methods in accuracy and speed.

Findings

Outperforms existing methods in variable selection and prediction.

Reduces computational time significantly.

Effective in both simulated and real data scenarios.

Abstract

In the field of big data analytics, the search for efficient subdata selection methods that enable robust statistical inferences with minimal computational resources is of high importance. A procedure prior to subdata selection could perform variable selection, as only a subset of a large number of variables is active. We propose an approach when both the size of the full dataset and the number of variables are large. This approach firstly identifies the active variables by applying a procedure inspired by random LASSO (Least Absolute Shrinkage and Selection Operator) and then selects subdata based on leverage scores to build a predictive model. Our proposed approach outperforms approaches that already exists in the current literature, including the usage of the full dataset, in both variable selection and prediction, while also exhibiting significant improvements in computing time. Simulation experiments as well as a real data application are provided.