Fused least absolute shrinkage and selection operator for credit scoring
- Authors
- Choi, Hosik; Koo, Ja-Yong; Park, Changyi
- Issue Date
- 24-7월-2015
- Publisher
- TAYLOR & FRANCIS LTD
- Keywords
- 62G08; 62F07; solution path; augmented Lagrangian function; LASSO
- Citation
- JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, v.85, no.11, pp.2135 - 2147
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
- Volume
- 85
- Number
- 11
- Start Page
- 2135
- End Page
- 2147
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/92988
- DOI
- 10.1080/00949655.2014.922685
- ISSN
- 0094-9655
- Abstract
- Credit scoring can be defined as the set of statistical models and techniques that help financial institutions in their credit decision makings. In this paper, we consider a coarse classification method based on fused least absolute shrinkage and selection operator (LASSO) penalization. By adopting fused LASSO, one can deal continuous as well as discrete variables in a unified framework. For computational efficiency, we develop a penalization path algorithm. Through numerical examples, we compare the performances of fused LASSO and LASSO with dummy variable coding.
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Collections - College of Political Science & Economics > Department of Statistics > 1. Journal Articles
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