This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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476
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Type:
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Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Risk Analysis
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| Abstract - #308822 |
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Title:
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Variable Selection in Discrete Choice Models
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Author(s):
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Yoshinori Kawasaki*+ and Masao Ueki
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Companies:
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The Institute of Statistical Mathematics and Yamagata University
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Address:
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10-3 Midori-cho, Tachikawa, Tokyo, International, 1908562, Japan
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Keywords:
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variable selection ;
intereaction ;
contingency table ;
pruning ;
data mining
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Abstract:
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To increase the predictive accuracy of a discrete choice model, it is often helpful to introduce arbitrary order of interactions in addition to main effect. However, a simple-minded generation of interactions suffers from combinatorial explosion, and the mixture of continuous and categorical variables even makes it difficult to define interaction itself. This paper starts with presenting a reasonable way to choose the promising interactions based on contingency table analysis. The proposed method is illustrated with an application to a medical data. In an application to credit scoring, we compare the performance of competing methods with other data mining tools such as CART-logit.
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Authors who are presenting talks have a * after their name.
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