Distributed variable screening for generalized linear models

TL;DR

This paper introduces a distributed variable screening method for generalized linear models that efficiently handles large datasets and covariate spaces, improving the reliability of variable selection by considering joint effects.

Contribution

The paper proposes a novel distributed screening approach using a sparsity-restricted surrogate likelihood estimator that accounts for joint covariate effects, with proven sure screening properties.

Findings

Method effectively includes true variables with high probability.

Simulation studies demonstrate strong finite sample performance.

Application to real data confirms practical utility.

Abstract

In this article, we develop a distributed variable screening method for generalized linear models. This method is designed to handle situations where both the sample size and the number of covariates are large. Specifically, the proposed method selects relevant covariates by using a sparsity-restricted surrogate likelihood estimator. It takes into account the joint effects of the covariates rather than just the marginal effect, and this characteristic enhances the reliability of the screening results. We establish the sure screening property of the proposed method, which ensures that with a high probability, the true model is included in the selected model. Simulation studies are conducted to evaluate the finite sample performance of the proposed method, and an application to a real dataset showcases its practical utility.