Model free feature screening with dependent variable in ultrahigh dimensional binary classification

DOI10.1016/J.SPL.2017.02.011zbMath1381.62178OpenAlexW2591314755MaRDI QIDQ2407775

Peng Lai, Fengli Song, Kaiwen Chen, Zhi Liu

Publication date: 6 October 2017

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2017.02.011

zbMATH Keywords

discriminant analysissure screening propertyfeature screeningranking consistency propertyultrahigh dimensional data

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) General nonlinear regression (62J02) Statistical ranking and selection procedures (62F07)

Related Items (3)

Conditional distance correlation screening for sparse ultrahigh-dimensional models ⋮ Robust model-free feature screening for ultrahigh dimensional surrogate data ⋮ Robust composite weighted quantile screening for ultrahigh dimensional discriminant analysis

Cites Work

• High-dimensional classification using features annealed independence rules
• Model free feature screening for ultrahigh dimensional data with responses missing at random
• The fused Kolmogorov filter: a nonparametric model-free screening method
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Feature Screening via Distance Correlation Learning
• Feature Selection for Varying Coefficient Models With Ultrahigh-Dimensional Covariates
• Regularization and Variable Selection Via the Elastic Net
• Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis
• The Kolmogorov filter for variable screening in high-dimensional binary classification
• Variable Selection in Nonparametric Varying-Coefficient Models for Analysis of Repeated Measurements

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