A note on the use of the K–S statistic as a measure of model strength
Abstract
The “K–S statistic” is a popular (in fact, almost a standard) measure of model strength for credit risk scoring models. This article defines the “K–S statistic” and explains how it is used in the context of testing statistical hypotheses. It also points out a common interpretation error made when using this statistic. This article was written with the credit marketer, who uses risk models in conjunction with his direct mail campaigns, in mind. But since any measure of risk model strength may also be used to measure the strength of a response model, it is hoped that this article is found useful by the rest of the direct marketing world who employ modeling to its advantage.
Citing Literature
Number of times cited according to CrossRef: 3
- Vitalie Bumacov, Arvind Ashta, Pritam Singh, Credit scoring: A historic recurrence in microfinance, Strategic Change, 10.1002/jsc.2165, 26, 6, (543-554), (2017).
- Guoping Zeng, A comparison study of computational methods of Kolmogorov–Smirnov statistic in credit scoring, Communications in Statistics - Simulation and Computation, 10.1080/03610918.2016.1249883, 46, 10, (7744-7760), (2017).
- Guoping Zeng, Invariant properties of logistic regression model in credit scoring under monotonic transformations, Communications in Statistics - Theory and Methods, 10.1080/03610926.2016.1193200, 46, 17, (8791-8807), (2016).




