ArticleJournal of the American Statistical Association2020
MODEL-FREE FORWARD SCREENING VIA CUMULATIVE DIVERGENCE.
Article in Journal of the American Statistical Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 44 citations in OpenAlex.
- Development of American Association for Thoracic Surgery Quality Gateway outcome models, analytics, and visualizations for quality assurance.The Journal of thoracic and cardiovascular surgery · 2025Article
- Feature Screening for High-Dimensional Variable Selection in Generalized Linear Models.Entropy (Basel, Switzerland) · 2023Article
- Quantile-Adaptive Sufficient Variable Screening by Controlling False Discovery.Entropy (Basel, Switzerland) · 2023Article
- A Model-free Variable Screening Method Based on Leverage Score.Journal of the American Statistical Association · 2023Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 3 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.