Fused least absolute shrinkage and selection operator for credit scoring

被引：3

作者：

Choi, Hosik ^{[1
]}

Koo, Ja-Yong ^{[2
]}

Park, Changyi ^{[3
]}

机构：

[1] Kyonggi Univ, Dept Appl & Informat Stat, Suwon 443760, Gyeonggi, South Korea

[2] Korea Univ, Dept Stat, Seoul 136701, South Korea

[3] Univ Seoul, Dept Stat, Seoul 130743, South Korea

来源：

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | 2015年 / 85卷 / 11期

基金：

新加坡国家研究基金会;

关键词：

62G08; 62F07; solution path; augmented Lagrangian function; LASSO;

D O I：

10.1080/00949655.2014.922685

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：2135 / 2147

页数：13

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