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A Bayesian Analysis of a Random Effects Small Business Loan Credit Scoring Model

Author(s): Patrick J. Farrell | Brenda MacGibbon | Thomas J. Tomberlin | Dale Doreen

Journal: Pakistan Journal of Statistics and Operation Research
ISSN 1816-2711

Volume: 7;
Issue: 2-Sp;
Date: 2011;
Original page

Keywords: Bayes Factors | Credit Scoring | MCMC | Variable Selection

One of the most important aspects of credit scoring is constructing a model that has low misclassification rates and is also flexible enough to allow for random variation. It is also well known that, when there are a large number of highly correlated variables as is typical in studies involving questionnaire data, a method must be found to reduce the number of variables to those that have high predictive power. Here we propose a Bayesian multivariate logistic regression model with both fixed and random effects for small business loan credit scoring and a variable reduction method using Bayes factors. The method is illustrated on an interesting data set based on questionnaires sent to loan officers in Canadian banks and venture capital companies
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