Model-Free Forward Screening Via Cumulative Divergence

Tingyou Zhou, Liping Zhu, Chen Xu, Runze Li

Research output: Contribution to journalArticlepeer-review

Abstract

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. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)1393-1405
Number of pages13
JournalJournal of the American Statistical Association
Volume115
Issue number531
DOIs
StatePublished - Jul 2 2020

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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