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