| Titre : | Bound and collapse Bayesian reject inference for credit scoring (2012) |
| Auteurs : | G. G. Chen, Auteur ; T. Åstebro, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | Journal of the operational research society (JORS) (Vol. 63 N° 10, Octobre 2012) |
| Article en page(s) : | pp. 1374–1387 |
| Note générale : | operational research |
| Langues : | Anglais |
| Index. décimale : | 001.424 |
| Tags : | statistics ; credit scoring ; Bayesian ; reject inference ; missing data |
| Résumé : | Reject inference is a method for inferring how a rejected credit applicant would have behaved had credit been granted. Credit-quality data on rejected applicants are usually missing not at random (MNAR). In order to infer credit-quality data MNAR, we propose a flexible method to generate the probability of missingness within a model-based bound and collapse Bayesian technique. We tested the method's performance relative to traditional reject-inference methods using real data. Results show that our method improves the classification power of credit scoring models under MNAR conditions. |
| Note de contenu : |
In an earlier version of this article the title was incorrect. The correct title is shown in this final version of the article.
Corrected online: 12 January 2012 |
| DEWEY : | 001.424 |
| ISSN : | 0160-5682 |
| En ligne : | http://www.palgrave-journals.com/jors/journal/v63/n10/abs/jors2011149a.html |
