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Activity Number: 245
Type: Contributed
Date/Time: Monday, August 4, 2014 : 2:00 PM to 2:45 PM
Sponsor: Section for Statistical Programmers and Analysts
Abstract #313981
Title: Information Value Statistic and Predictors for Logistic Regression
Author(s): Bruce Lund*+
Companies: Marketing Associates
Keywords: Logistic Regression ; Information Value Statistic ; Optimal Binning ; Direct Marketing ; Weight-of-Evidence ; Log Likelihood
Abstract:

In preparing predictor variables for a binary logistic regression model it is common to collapse the levels of a nominal or discrete-valued predictor X to achieve parsimony while maintaining predictive power. Once the levels have been binned, the binned predictor is transformed to weight-of-evidence (WOE) coding for usage as a predictor in the model.

In the first section of the paper an algorithm is given for collapsing the levels of a nominal or discrete-valued predictor X for predicting binary Y so that information value (IV) is maximized at each step in the collapsing. The algorithm allows the ordering of X to be maintained during the collapsing if X is ordinal. This algorithm is coded in SASĀ®.

In the second section a process is given to simulate the probability distribution of IV under the assumption of no association between X and Y. Since, in practice, IV does not have a parametric probability distribution, this simulation provides a tool to reject non-significant IV.


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