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A Bayesian Analysis of a Random Effects Small Business Loan Credit Scoring Model
Author(s):
1. Patrick J. Farrell: School of Mathematics and Statistics Carleton University, 1125 Colonel By Drive Ottawa, Ontario, CANADA
2. Brenda MacGibbon: Département de mathématiques Université du Québec à Montréal C.P. 8888, Succursale Centre-Ville Montréal, Québec, CANADA
3. Thomas J. Tomberlin: Sprott School of Business Carleton University, 1125 Colonel By Drive Ottawa, Ontario, CANADA
4. Dale Doreen: Department of Decision Sciences and Management Information Systems John Molson School of Business Concordia University 1455 de Maisonneuve Blvd. West Montréal, Québec, CANADA
Abstract:
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.
Page(s): 433-449
DOI: DOI not available
Published: Journal: Pakistan Journal of Statistics and Operation Research, Volume: 7, Issue: 2, Year: 2011
Keywords:
Variable selection , MCMC , Credit Scoring , Bayes Factors
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