The dissertation tackles the problem of credit scoring. This term is used to describe statistical
methods used for classifying applicants for credit into good and bad risk classes. Such methods
have become increasingly important with the dramatic growth in consumer credit in recent years.
In addition, the advances in computer technology permits to treat large data sets. The credit area
is a natural one for application of statistical ideas. In this dissertation we describe the construction of a credit
scoring system using $k$ nearest neighbour method and another using nonparametric estimation
of density, in particular kernel estimator. We compare results with two methods of the SAS software.
Date of Award | Jun 1999 |
---|
Original language | French |
---|
Supervisor | Jean Paul Rasson (Supervisor) |
---|
L'accord de crédits aux particuliers à l'aide de méthodes non paramétriques
Pirçon, J. (Author). Jun 1999
Student thesis: Master types › Master in Mathematics