ArticleJournal of the American Statistical Association2012
Feature Screening via Distance Correlation Learning.
Article in Journal of the American Statistical Association, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 120 papers.
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Who cites it
120 citing papers in PubMed.
- Predictions and cross-center validation of IDH status and 1p/19q status in adult diffuse glioma with non-invasive magnetic resonance spectroscopy.Magnetic resonance letters · 2027Article
- A benchmarking study of feature screening approaches across type 1 diabetes omics studies classification settings.PLoS computational biology · 2026Article
- Predictor-Assisted Nonparametric Graphical Models With Multivariate Error-Prone Data.Statistics in medicine · 2026Article
- PAGE: an R package for network detection of multivariate error-prone gene expression data with the availability of auxiliary information.BMC bioinformatics · 2026Article
- Nonlinear kernel-based high-dimensional inference for set-based genetic association studies.Briefings in bioinformatics · 2026Article
- Assumption-Agnostic Deep Learning Framework for Holistic Clinical Trial Monitoring.Therapeutic innovation & regulatory science · 2026Article
- Regression-based Modeling of Spearman's Rho for Longitudinal Metabolomics and Mental Wellness in Breast Cancer Patients.bioRxiv : the preprint server for biology · 2026Article
- Prognostic biomarker discovery in pancreatic cancer through hybrid ensemble feature selection and multi-omics data.BioData mining · 2026Article
- Integrating Distance Correlation and Adaptive Weighting with RBF Kernel Transformations: A Novel Feature Selection Framework with Application to ECG Arrhythmia Detection.Bioengineering (Basel, Switzerland) · 2026Article
- Identifying the Best Predictive Biomarker in Pharmacogenomics Through Multiple Comparisons With the Best.Biometrical journal. Biometrische Zeitschrift · 2026Article
- A multi-omics framework for survival mediation analysis of high-dimensional proteogenomic data.PLoS computational biology · 2026Article
- A copula based supervised filter for feature selection in machine learning driven diabetes risk prediction.Scientific reports · 2026Article
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- Detection of LUAD-Associated Genes Using Wasserstein Distance in Multiomics Feature Selection.Bioengineering (Basel, Switzerland) · 2025Article
- Uncertainty Quantification in Epigenetic Clocks via Conformalized Quantile Regression.Genetic epidemiology · 2025Article
- Optimizing Quality Tolerance Limits Monitoring in Clinical Trials Through Machine Learning Methods.Therapeutic innovation & regulatory science · 2025Article
- Uncertainty quantification in epigenetic clocks via conformalized quantile regression.medRxiv : the preprint server for health sciences · 2025Article
- Universally Consistent K-Sample Tests via Dependence Measures.Statistics & probability letters · 2025Article
- A powerful penalized multinomial logistic regression approach.Computational statistics · 2025Article
60 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
This paper is concerned with screening features in ultrahigh dimensional data analysis, which has become increasingly important in diverse scientific fields. We develop a sure independence screening procedure based on the distance correlation (DC-SIS, for short). The DC-SIS can be implemented as easily as the sure independence screening procedure based on the Pearson correlation (SIS, for short) proposed by Fan and Lv (2008). However, the DC-SIS can significantly improve the SIS. Fan and Lv (2008) established the sure screening property for the SIS based on linear models, but the sure screening property is valid for the DC-SIS under more general settings including linear models. Furthermore, the implementation of the DC-SIS does not require model specification (e.g., linear model or generalized linear model) for responses or predictors. This is a very appealing property in ultrahigh dimensional data analysis. Moreover, the DC-SIS can be used directly to screen grouped predictor variables and for multivariate response variables. We establish the sure screening property for the DC-SIS, and conduct simulations to examine its finite sample performance. Numerical comparison indicates that the DC-SIS performs much better than the SIS in various models. We also illustrate the DC-SIS through 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.