Feature Screening for Ultrahigh Dimensional Categorical Data With Applications
Bibliographic Data
| ID | 19417423 |
|---|---|
| Authors | Danyang Huang (0000-0002-4053-2680, King University), Runze Li (0000-0002-0154-2202, King University), Hansheng Wang (0000-0003-2386-0209, King University) |
| Year | 2014 |
| Volume | 32 |
| Issue | 2 |
| Pages | 237-244 |
| Publication date | 2014-04-03 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2013.863158 |
| PMID | 25328278 |
| PMCID | PMC4197855 |
| OpenAlex | W1998244242 |
| Language | EN |
| Citations received | 1 |
| References cited | 20 |
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
Categorical variable · Data mining · Econometrics · Machine learning · Statistics · Computer Science · Image and Signal Denoising Methods · Machine Learning and Data Classification · Mathematics · Statistical Methods and Inference · Artificial Intelligence
| Citations per year | 0.25 |
|---|---|
| Citation span | 2022 - 2022 (1) |
| Highly cited | No |