Model-Free Forward Screening Via Cumulative Divergence

• 成果类型: Article
• 署名作者: Zhou, Tingyou; Zhu, Liping; Xu, Chen; Li, Runze
• 署名单位: Zhejiang University of Finance & Economics; Renmin University of China; University of Ottawa; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2019.1632078
• 发表日期: 2020
• 页码: 1393-1405
• 关键词: nonconcave penalized likelihood; diverging number; adaptive lasso; regression; selection

摘要

Feature screening plays an important role in the analysis of ultrahigh dimensional data. Due to complicated model structure and high noise level, existing screening methods often suffer from model misspecification and the presence of outliers. To address these issues, we introduce a new metric named cumulative divergence (CD), and develop a CD-based forward screening procedure. This forward screening method is model-free and resistant to the presence of outliers in the response. It also incorporates the joint effects among covariates into the screening process. With a data-driven threshold, the new method can automatically determine the number of features that should be retained after screening. These merits make the CD-based screening very appealing in practice. Under certain regularity conditions, we show that the proposed method possesses sure screening property. The performance of our proposal is illustrated through simulations and a real data example. for this article are available online.