Model Selection for Consumer Loan Application Data
Loan applications at banks are often long, requiring the applicant
to provide large amounts of data. Is all of it necessary? Can we
save the applicant some frustration and the bank some expense by
using only a subset of the relevant variables? To answer this
question, I have attempted to model the current loan approval
process at a particular bank.
I have used several model selection techniques for logistic
regression, including stepwise regression, Occam's Window, Markov
Chain Monte Carlo Model Composition (Raftery, Madigan, and Hoeting,
1993), and Bayesian Random Searching. The resulting models largely
agree upon a subset of only one-third of the original variables.
Keywords:Bayes Factor, Model Uncertainty, Markov Chain Monte
Carlo, Logistic Regression
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