Name
Title | ||
|---|---|---|
Enhancing two-stage modelling methodology for loss given default with support vector machines. |
Abstract | ||
|---|---|---|
•A large sample of retail loss data is used to investigate loss given default models.•Support vector machine works as the classifier in the two-stage modelling approach.•The two-stage model becomes more predictive with support vector machine applied.•Regression models play a less influential role in two-stage models. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.ejor.2017.05.017 | European Journal of Operational Research |
Keywords | Field | DocType |
Risk analysis,Loss given default modelling,Two-stage model,Support vector machine | Least squares support vector machine,Regression,Regression analysis,Computer science,Support vector machine,Loss given default,Artificial intelligence,Relevance vector machine,Logistic regression,Machine learning,Bounded function | Journal |
Volume | Issue | ISSN |
263 | 2 | 0377-2217 |
Citations | PageRank | References |
4 | 0.46 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yao Xiao | 1 | 64 | 9.74 |
| Jonathan Crook | 2 | 197 | 14.31 |
| Galina Andreeva | 3 | 61 | 6.13 |