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A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior
Abstract
Since the early work of Durand (1941), there has been considerable interest in using quantitative models of consumer credit behavior for credit-granting decisions. Most models are based on the concept of “scoring” by use of weights usually determined as statistically significant coefficients of some linear statistical model, frequently the linear discriminant model. It is the purpose of this note, however, to propose maximum likelihood estimation of the logit model as an alternative, and to compare the two models in a “scoring experiment.”
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Cited by
Statistical Classification Methods in Consumer Credit Scoring: A Review
Hand, D. J., & Henley, W. E. (1997) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.1997.00078.x · Cited by 1,042
Credit risk assessment with a multistage neural network ensemble learning approach
YU, L., WANG, S., & LAI, K. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.01.009 · Cited by 310
Spatial dependence in credit risk and its improvement in credit scoring
Fernandes, G. B., & Artes, R. (2016) · European Journal of Operational Research · doi:10.1016/j.ejor.2015.07.013 · Cited by 90
Metadata
- Authors
- Wiginton, J. C.
- Venue
- The Journal of Financial and Quantitative Analysis
- Publisher
- JSTOR
- Published
- 1980-09
- Volume
- 15
- Issue
- 3
- Pages
- 757
- ISSN
- 0022-1090 (ISSN-L)
- OpenAlex
- W2158068969
- Cited by
- 435
- References
- 17
- FWCI
- 11.83
- Type
- journal article
- Language
- en
- Open access
- closed
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