Ranking-Based Variable Selection for high-dimensional data

DOI10.5705/ss.202017.0139zbMath1460.62033OpenAlexW3094221292WikidataQ129239381 ScholiaQ129239381MaRDI QIDQ5134486

R. Baranowski, Piotr Fryzlewicz, Yining Chen

Publication date: 16 November 2020

Published in: Statistica Sinica (Search for Journal in Brave)

Full work available at URL: http://eprints.lse.ac.uk/90233/

zbMATH Keywords

bootstrapsubset selectionvariable screeningstability selection

Mathematics Subject Classification ID

Bayesian inference (62F15) Bootstrap, jackknife and other resampling methods (62F40) Statistical ranking and selection procedures (62F07)

Related Items

A nonparametric procedure for linear and nonlinear variable screening, Model-Free Variable Selection With Matrix-Valued Predictors, Exploiting Disagreement Between High-Dimensional Variable Selectors for Uncertainty Visualization, rbvs

Uses Software

• R
• SIS
• not

Cites Work

• Unnamed Item
• Unnamed Item
• Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection
• Model-Free Feature Screening for Ultrahigh-Dimensional Data
• Sure independence screening in generalized linear models with NP-dimensionality
• Nearly unbiased variable selection under minimax concave penalty
• Marginal empirical likelihood and sure independence feature screening
• Robust rank correlation based screening
• Brownian distance covariance
• Using the bootstrap to quantify the authority of an empirical ranking
• Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
• Sup-norm convergence rate and sign concentration property of Lasso and Dantzig estimators
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• Effect of heavy tails on ultra high dimensional variable ranking methods
• Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Stability Selection
• High Dimensional Variable Selection via Tilting
• Feature Screening via Distance Correlation Learning
• Martingale Difference Correlation and Its Use in High-Dimensional Variable Screening
• Variable Selection with Error Control: Another Look at Stability Selection
• Probability Inequalities for Sums of Bounded Random Variables
• Resampling Fewer Than n Observations: Gains, Losses, and Remedies for Losses
• Adaptive choice of bootstrap sample sizes