# Penalized linear regression with high-dimensional pairwise screening

## TL;DR

This paper introduces a novel high-dimensional variable screening method that incorporates pairwise covariate effects, improving selection accuracy by considering dependence among variables and combining it with existing screening techniques.

## Contribution

It develops a new theoretical framework for pairwise correlation distribution and proposes a combined screening and penalization method for better variable selection.

## Key findings

- Method improves prediction accuracy.
- Method enhances variable selection precision.
- Theoretical results support the screening approach.

## Abstract

In variable selection, most existing screening methods focus on marginal effects and ignore dependence between covariates. To improve the performance of selection, we incorporate pairwise effects in covariates for screening and penalization. We achieve this by studying the asymptotic distribution of the maximal absolute pairwise sample correlation among independent covariates. The novelty of the theory is in that the convergence is with respect to the dimensionality $p$, and is uniform with respect to the sample size $n$. Moreover, we obtain an upper bound for the maximal pairwise R squared when regressing the response onto two different covariates. Based on these extreme value results, we propose a screening procedure to detect covariates pairs that are potentially correlated and associated with the response. We further combine the pairwise screening with Sure Independence Screening and develop a new regularized variable selection procedure. Numerical studies show that our method is very competitive in terms of both prediction accuracy and variable selection accuracy.