Invariant properties of logistic regression model in credit scoring under monotonic transformations. Issue 17 (2nd September 2017)

• Record Type: Journal Article
• Authors: Zeng, Guoping
• Is Part Of: Communications in statistics. Volume 46:Issue 17(2017)
• Page Start: 8791
• Page End: 8807
• Publication Date: 2017-09-02
• Subjects: Information value -- Kolmogorov statistics -- Lift table -- Logistic regression -- Monotonic transformation -- Maximum likelihood estimate -- Mutual information.; 62-07; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2016.1193200
• Languages: English
• ISSNs: 0361-0926

Abstract

ABSTRACT: Monotonic transformations of explanatory continuous variables are often used to improve the fit of the logistic regression model to the data. However, no analytic studies have been done to study the impact of such transformations. In this paper, we study invariant properties of the logistic regression model under monotonic transformations. We prove that the maximum likelihood estimates, information value, mutual information, Kolmogorov–Smirnov (KS) statistics, and lift table are all invariant under certain monotonic transformations.