Distributed variable screening for generalized linear models

Tianbo Diao, Bo Li, Lianqiang Qu and Liuquan Sun

Computational Statistics & Data Analysis, 2025, vol. 211, issue C

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.

Keywords: Distributed learning; Generalized linear models; Massive data; Variable screening (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:211:y:2025:i:c:s0167947325000799

DOI: 10.1016/j.csda.2025.108203

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