Volume 6, Issue 4
Article

A note on the use of the K–S statistic as a measure of model strength

Rosana P. Thrasher

Rosie Thrasher is a statistical specialist at TRW Information Systems and Services She holds both a Masters and a PhD degree in the field of mathematical statistics from the University of California, Irvine and is a member of the Institute of Mathematical Statistics, the American Statistical Association and the Direct Marketing Association Dr Thrasher joined TRW in 1987, with extensive teaching and consulting experience As TRW's resident expert in CART, she contributed an article discussing this tree‐structured list segmentation methodology to a past issue of JDM She has also published in other academic journals and continues to develop effective new techniques in the field of modeling.

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First published: Autumn (Fall) 1992
Citations: 3

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.

Number of times cited according to CrossRef: 3

  • Credit scoring: A historic recurrence in microfinance, Strategic Change, 10.1002/jsc.2165, 26, 6, (543-554), (2017).
  • 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).
  • 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).

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