Nonparametric independence screening via favored smoothing bandwidth
From MaRDI portal
Publication:1643789
DOI10.1016/j.jspi.2017.11.006zbMath1432.62093arXiv1711.10411OpenAlexW2768777175MaRDI QIDQ1643789
Yichao Wu, Yang Feng, Leonard A. Stefanski
Publication date: 20 June 2018
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1711.10411
Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12) Asymptotic properties of nonparametric inference (62G20)
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Cites Work
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- 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
- Robust rank correlation based screening
- Variable selection in nonparametric additive models
- High-dimensional additive modeling
- Weak convergence and empirical processes. With applications to statistics
- Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models
- A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)
- UNIFORM CONVERGENCE RATES FOR KERNEL ESTIMATION WITH DEPENDENT DATA
- Sliced Inverse Regression for Dimension Reduction
- Calibrating the Degrees of Freedom for Automatic Data Smoothing and Effective Curve Checking
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Feature Screening via Distance Correlation Learning
- Feature Selection for Varying Coefficient Models With Ultrahigh-Dimensional Covariates
- Variable Selection in Nonparametric Classification Via Measurement Error Model Selection Likelihoods
- Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Varying Coefficient Models
- Automatic structure recovery for additive models
- The Kolmogorov filter for variable screening in high-dimensional binary classification
- Exploration, normalization, and summaries of high density oligonucleotide array probe level data