A model-free conditional screening approach via sufficient dimension reduction
From MaRDI portal
Publication:4988818
DOI10.1080/10485252.2020.1834554zbMath1469.62236OpenAlexW3094918129MaRDI QIDQ4988818
Xuerong Meggie Wen, Lei Huo, Zhou Yu
Publication date: 19 May 2021
Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10485252.2020.1834554
Nonparametric regression and quantile regression (62G08) Multivariate analysis (62H99) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05)
Cites Work
- Unnamed Item
- Model-Free Feature Screening for Ultrahigh-Dimensional Data
- Sure independence screening in generalized linear models with NP-dimensionality
- Local independence feature screening for nonparametric and semiparametric models by marginal empirical likelihood
- Marginal empirical likelihood and sure independence feature screening
- On almost linearity of low dimensional projections from high dimensional data
- Dimension reduction for nonelliptically distributed predictors
- Nonparametric feature screening
- Partial projective resampling method for dimension reduction: with applications to partially linear models
- Sufficient dimension reduction in regressions with categorical predictors
- Testing predictor contributions in sufficient dimension reduction.
- Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
- Model-free conditional screening via conditional distance correlation
- Conditional SIRS for nonparametric and semiparametric models by marginal empirical likelihood
- On dimension folding of matrix- or array-valued statistical objects
- The fused Kolmogorov filter: a nonparametric model-free screening method
- Sure independence screening and compressed random sensing
- Forward Regression for Ultra-High Dimensional Variable Screening
- Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models
- Factor profiled sure independence screening
- Dimension reduction for non-elliptically distributed predictors: second-order methods
- On Directional Regression for Dimension Reduction
- Sliced Inverse Regression for Dimension Reduction
- Reweighting to Achieve Elliptically Contoured Covariates in Regression
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- On Partial Sufficient Dimension Reduction With Applications to Partially Linear Multi-Index Models
- Likelihood-Based Sufficient Dimension Reduction
- Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis
- The Kolmogorov filter for variable screening in high-dimensional binary classification
- Marginal tests with sliced average variance estimation
- Correlation Pursuit: Forward Stepwise Variable Selection for Index Models
- Group screening for ultra-high-dimensional feature under linear model
- Comment
This page was built for publication: A model-free conditional screening approach via sufficient dimension reduction