Reject inference methods in credit scoring.

The granting process is based on the probability that the applicant will refund his/her loan given his/her characteristics. This probability, also called score, is learnt based on a dataset in which rejected applicants are excluded. Thus, the population on which the score is used is different from the learning population.

A generalized discriminant rule when training population and test population differ on their descriptive parameters.

Standard discriminant analysis methods make the assumption that both the labeled sample used to estimate the discriminant rule and the nonlabeled sample on which this rule is applied arise from the same population. In this work, we consider the case where the two populations are slightly different. In the multinormal context, we establish that both populations are linked through linear mapping.