Nonparametric feature screening

DOI10.1016/j.csda.2013.05.016zbMath1471.62119OpenAlexW631668381MaRDI QIDQ1615096

Jing Sun, Lu Lin, Li Xing Zhu

Publication date: 2 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2013.05.016

zbMATH Keywords

nonparametric modelfeature screeningultrahigh-dimensional datamarginal utilityfunction-correlation

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Measures of association (correlation, canonical correlation, etc.) (62H20)

Related Items (16)

Partition-based feature screening for categorical data via RKHS embeddings ⋮ Conditional sure independence screening by conditional marginal empirical likelihood ⋮ Adaptive conditional feature screening ⋮ Model-free feature screening via a modified composite quantile correlation ⋮ Model-free conditional screening via conditional distance correlation ⋮ Nonparametric variable selection and its application to additive models ⋮ A selective overview of feature screening for ultrahigh-dimensional data ⋮ Ultrahigh dimensional feature screening for additive model with multivariate response ⋮ Conditional SIRS for nonparametric and semiparametric models by marginal empirical likelihood ⋮ Independent feature screening for ultrahigh-dimensional models with interactions ⋮ Feature selection for clustering using instance-based learning by exploring the nearest and farthest neighbors ⋮ Sure feature screening for high-dimensional dichotomous classification ⋮ Ultrahigh dimensional time course feature selection ⋮ A note on quantile feature screening via distance correlation ⋮ Grouped variable screening for ultra-high dimensional data for linear model ⋮ A model-free conditional screening approach via sufficient dimension reduction

Cites Work

• Model-Free Feature Screening for Ultrahigh-Dimensional Data
• Sure independence screening in generalized linear models with NP-dimensionality
• Robust rank correlation based screening
• On Distribution-Weighted Partial Least Squares with Diverging Number of Highly Correlated Predictors
• Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models
• Sufficient dimension reduction through discretization-expectation estimation
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Feature Screening via Distance Correlation Learning

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