%0 Report %T Bound and Collapse Bayesian Reject Inference for Credit Scoring %+ Groupement de Recherche et d'Etudes en Gestion à HEC (GREGH) %+ Department of Management Sciences %A Astebro, Thomas B. %A Chen, Gongyue %Z Mimeo, 2010 %8 2010 %D 2010 %K Credit scoring %K reject inference %K missing not at random %K Bayesian inference %Z Humanities and Social Sciences/Business administration/domain_shs.gestion.stratReports %X 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. %G English %L hal-00655036 %U https://hec.hal.science/hal-00655036 %~ SHS %~ HEC %~ CNRS %~ LARA