RaSE: A Variable Screening Framework via Random Subspace Ensembles
DOI10.1080/01621459.2021.1938084zbMath1514.68272arXiv2102.03892OpenAlexW3172604113MaRDI QIDQ6107221
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Publication date: 3 July 2023
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2102.03892
high-dimensional datavariable selectionvariable screeningsure screening propertyensemble learningrandom subspace methodrank consistency
Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Statistical aspects of big data and data science (62R07) Computational aspects of data analysis and big data (68T09)
Cites Work
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- Model-Free Feature Screening for Ultrahigh-Dimensional Data
- Nearly unbiased variable selection under minimax concave penalty
- Variable selection for general index models via sliced inverse regression
- Nonparametric independence screening and structure identification for ultra-high dimensional longitudinal data
- Principled sure independence screening for Cox models with ultra-high-dimensional covariates
- Interaction pursuit in high-dimensional multi-response regression via distance correlation
- Nonparametric screening under conditional strictly convex loss for ultrahigh dimensional sparse data
- The fused Kolmogorov filter: a nonparametric model-free screening method
- Extended BIC for small-n-large-P sparse GLM
- Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models
- Extended Bayesian information criteria for model selection with large model spaces
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Regularization after retention in ultrahigh dimensional linear regression models
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Feature Screening via Distance Correlation Learning
- Interaction Screening for Ultrahigh-Dimensional Data
- Martingale Difference Correlation and Its Use in High-Dimensional Variable Screening
- Model-Free Forward Screening Via Cumulative Divergence
- An iterative approach to distance correlation-based sure independence screening
- Robust Variable and Interaction Selection for Logistic Regression and General Index Models
- A Generic Sure Independence Screening Procedure
- Regularization and Variable Selection Via the Elastic Net
- Random-projection Ensemble Classification
- Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis
- High Dimensional Ordinary Least Squares Projection for Screening Variables
- The Kolmogorov filter for variable screening in high-dimensional binary classification
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