Unified mean-variance feature screening for ultrahigh-dimensional regression
DOI10.1007/s00180-021-01184-2zbMath1505.62417OpenAlexW4205886992MaRDI QIDQ2095721
Liming Wang, Xiaoqing Wang, Peng Lai, Xingxiang Li
Publication date: 15 November 2022
Published in: Computational Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00180-021-01184-2
mean-variancesure screening propertyultrahigh-dimensional datakernel smoothing estimateunified marginal utility
Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Generalized linear models (logistic models) (62J12)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Model-Free Feature Screening for Ultrahigh-Dimensional Data
- Nearly unbiased variable selection under minimax concave penalty
- The Adaptive Lasso and Its Oracle Properties
- Robust rank correlation based screening
- Principled sure independence screening for Cox models with ultra-high-dimensional covariates
- High-dimensional classification using features annealed independence rules
- Ultrahigh dimensional feature screening via projection
- Fused mean-variance filter for feature screening
- Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
- The fused Kolmogorov filter: a nonparametric model-free screening method
- Forward Regression for Ultra-High Dimensional Variable Screening
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Model-Free Feature Screening for Ultrahigh Dimensional Data through a Modified Blum-Kiefer-Rosenblatt Correlation
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Feature Screening via Distance Correlation Learning
- Feature Selection for Varying Coefficient Models With Ultrahigh-Dimensional Covariates
- The Sparse MLE for Ultrahigh-Dimensional Feature Screening
- Composite Coefficient of Determination and Its Application in Ultrahigh Dimensional Variable Screening
- Regularization and Variable Selection Via the Elastic Net
- Probability Inequalities for Sums of Bounded Random Variables
- Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis
- The Kolmogorov filter for variable screening in high-dimensional binary classification
- Model Selection and Estimation in Regression with Grouped Variables
This page was built for publication: Unified mean-variance feature screening for ultrahigh-dimensional regression