Robust rank screening for ultrahigh dimensional discriminant analysis

DOI10.1007/s11222-016-9637-2zbMath1505.62095OpenAlexW2267080915MaRDI QIDQ518270

Xingxiang Li, Fengli Song, Peng Lai, Guosheng Cheng, Jun Yu

Publication date: 28 March 2017

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11222-016-9637-2

zbMATH Keywords

sure screening propertyfeature screeningrobust property of rankultrahigh dimensional discriminant analysis

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items

Conditional distance correlation screening for sparse ultrahigh-dimensional models ⋮ Variance ratio screening for ultrahigh dimensional discriminant analysis ⋮ Robust composite weighted quantile screening for ultrahigh dimensional discriminant analysis ⋮ A consistent variable selection method in high-dimensional canonical discriminant analysis

Uses Software

• penalizedLDA

Cites Work

• Unnamed Item
• Model-Free Feature Screening for Ultrahigh-Dimensional Data
• Sure independence screening in generalized linear models with NP-dimensionality
• Robust rank correlation based screening
• High-dimensional classification using features annealed independence rules
• Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations
• 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
• Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models
• Penalized Classification using Fisher’s Linear Discriminant
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Feature Screening via Distance Correlation Learning
• 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