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Abstract Details

Activity Number: 506
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Risk Analysis
Abstract - #306146
Title: Grouping Variables and Collapsing Levels in Credit Scoring
Author(s): Yoshinori Kawasaki*+ and Masao Ueki
Companies: Institute of Statistical Mathematics and Yamagata University
Address: 10-3 Midori-Cho, Tokyo, International, 1908562, Japan
Keywords: Automatic grouping ; Lasso ; Smooth-thresholding ; Variable selection ; Credit scoring

In building a credit scoring model, we often find some variables having comparable explanatory power. In such a case, it is useful to consider overlapping parameters of which the values are similar. The problem is how to group the variables systematically. We may naturally consider interaction terms. If it happens that all the interaction terms from two categorical variables X and Y have similar effects, then we collapse the conditioning on Y and use X only. This is again the problem of grouping the variables. This paper illustrates how the smooth-thresholding estimating equation (STEE) method works well in automatic variable grouping. STEE simultaneously enables variable selection that can yield sparse solution. It also enjoys the oracle property while does not involve any convex optimization.

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