Partition-based feature screening for categorical data via RKHS embeddings

Jun Lu, Lu Lin and WenWu Wang

Computational Statistics & Data Analysis, 2021, vol. 157, issue C

Abstract: This paper proposes a new screening procedure for the ultrahigh dimensional data with a categorical response. By exploiting the group structure among predictors, a new partition-based screening approach is developed via the reproducing kernel Hilbert space (RKHS) embeddings in the maximum mean discrepancy framework. Consequently, the new method is able to identify the influential group of predictors that may be overlooked by the marginal screening methods. Moreover, by using the RKHS embedding, the new ranking index has a very simple form, and thus can be evaluated easily. As a by-product, the new method is model-free without specifying any relationship between the predictors and the response. The sure screening property of the proposed method is proved and the effectiveness of the new method is also illustrated via numerical studies and a real data analysis.

Keywords: Feature screening; Partition-based; Categorical data; RKHS (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947321000104
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:157:y:2021:i:c:s0167947321000104

DOI: 10.1016/j.csda.2021.107176

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().