title: Feature Screening for Ultrahigh Dimensional Categorical Data with Applications.
abstract: Ultrahigh dimensional data with both categorical responses and categorical covariates are frequently encountered in the analysis of big data, for which feature screening has become an indispensable statistical tool. We propose a Pearson chi-square based feature screening procedure for categorical response with ultrahigh dimensional categorical covariates. The proposed procedure can be directly applied for detection of important interaction effects. We further show that the proposed procedure possesses screening consistency property in the terminology of Fan and Lv (2008). We investigate the finite sample performance of the proposed procedure by Monte Carlo simulation studies, and illustrate the proposed method by two empirical datasets.
authors.affiliation: Peking University & Pennsylvania State University.
authors.forename: Danyang
authors.initials: D
authors.lastname: Huang
authors.affiliation: Peking University & Pennsylvania State University.
authors.forename: Runze
authors.initials: R
authors.lastname: Li
authors.affiliation: Peking University & Pennsylvania State University.
authors.forename: Hansheng
authors.initials: H
authors.lastname: Wang
keywords: Feature Screening; Pearson’s Chi-Square Test; Screening Consistency; Search Engine Marketing; Text Classification; Ultrahigh Dimensional Data