Fused least absolute shrinkage and selection operator for credit scoring

Hosik Choi, Ja Yong Koo, Changyi Park

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish
Pages (from-to)2135-2147
Number of pages13
JournalJournal of Statistical Computation and Simulation
Volume85
Issue number11
DOIs
StatePublished - 24 Jul 2015

Keywords

  • LASSO
  • augmented Lagrangian function
  • solution path

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