Publication
Title
Robust and sparse logistic regression
Author
Abstract
Logistic regression is one of the most popular statistical techniques for solving (binary) classification problems in various applications (e.g. credit scoring, cancer detection, ad click predictions and churn classification). Typically, the maximum likelihood estimator is used, which is very sensitive to outlying observations. In this paper, we propose a robust and sparse logistic regression estimator where robustness is achieved by means of the gamma-divergence. An elastic net penalty ensures sparsity in the regression coefficients such that the model is more stable and interpretable. We show that the influence function is bounded and demonstrate its robustness properties in simulations. The good performance of the proposed estimator is also illustrated in an empirical application that deals with classifying the type of fuel used by cars.
Language
English
Source (journal)
Advances in data analysis and classification. - Berlin, 2007, currens
Publication
Heidelberg : Springer heidelberg , 2023
ISSN
1862-5347 [print]
1862-5355 [online]
DOI
10.1007/S11634-023-00572-4
Volume/pages
(2023) , p. 1-17
ISI
001119654200001
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 09.01.2024
Last edited 19.01.2024
To cite this reference