ArticleJournal of the American Statistical Association2023
A Model-free Variable Screening Method Based on Leverage Score.
Article in Journal of the American Statistical Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A benchmarking study of feature screening approaches across type 1 diabetes omics studies classification settings.PLoS computational biology · 2026Article
- FlashDeconv reveals resolution horizons in atlas-scale spatial transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Uncertainty Quantification in Epigenetic Clocks via Conformalized Quantile Regression.Genetic epidemiology · 2025Article
- 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
- Adaptive Use of Co-Data Through Empirical Bayes for Bayesian Additive Regression Trees.Statistics in medicine · 2025Article
- Uncertainty quantification in epigenetic clocks via conformalized quantile regression.medRxiv : the preprint server for health sciences · 2025Article
- Smoothing splines approximation using Hilbert curve basis selection.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
With rapid advances in information technology, massive datasets are collected in all fields of science, such as biology, chemistry, and social science. Useful or meaningful information is extracted from these data often through statistical learning or model fitting. In massive datasets, both sample size and number of predictors can be large, in which case conventional methods face computational challenges. Recently, an innovative and effective sampling scheme based on leverage scores via singular value decompositions has been proposed to select rows of a design matrix as a surrogate of the full data in linear regression. Analogously, variable screening can be viewed as selecting rows of the design matrix. However, effective variable selection along this line of thinking remains elusive. In this article, we bridge this gap to propose a weighted leverage variable screening method by utilizing both the left and right singular vectors of the design matrix. We show theoretically and empirically that the predictors selected using our method can consistently include true predictors not only for linear models but also for complicated general index models. Extensive simulation studies show that the weighted leverage screening method is highly computationally efficient and effective. We also demonstrate its success in identifying carcinoma related genes using spatial transcriptome data.
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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.