Adjusted Pearson chi-square feature screening for multi-classification with ultrahigh dimensional data

DOI10.1007/s00184-017-0629-9zbMath1390.62113OpenAlexW2763137833MaRDI QIDQ1683647

Fangjiao Wan, Fang Fang, Lyu Ni

Publication date: 1 December 2017

Published in: Metrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00184-017-0629-9

zbMATH Keywords

variable selectionsure screening propertycontinuous and categorical covariatesdiverging classesPearson chi-square statistics

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items

Variable screening with missing covariates: a discussion of ‘statistical inference for nonignorable missing data problems: a selective review’ by Niansheng Tang and Yuanyuan Ju ⋮ Model-free feature screening for ultrahigh dimensional data via a Pearson chi-square based index ⋮ A distribution-free test of independence based on a modified mean variance index ⋮ Regularized quantile regression for ultrahigh-dimensional data with nonignorable missing responses ⋮ Feature screening for ultrahigh dimensional categorical data with covariates missing at random ⋮ Grouped feature screening for ultra-high dimensional data for the classification model

Uses Software

• C4.5

Cites Work

• Model-Free Feature Screening for Ultrahigh-Dimensional Data
• Sure independence screening in generalized linear models with NP-dimensionality
• High-dimensional classification using features annealed independence rules
• Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
• The fused Kolmogorov filter: a nonparametric model-free screening method
• Entropy-based model-free feature screening for ultrahigh-dimensional multiclass classification
• Forward Regression for Ultra-High Dimensional Variable Screening
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Feature Screening via Distance Correlation Learning
• Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Varying Coefficient Models
• Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis
• The Kolmogorov filter for variable screening in high-dimensional binary classification