This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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182
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Type:
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Contributed
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Date/Time:
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Monday, August 2, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #307735 |
Title:
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Logistic Model Selection via Association Rules Analysis
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Author(s):
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Pannapa Changpetch*+ and Dennis Lin
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Companies:
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Penn State and Penn State
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Address:
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142 Suburban Ave., State College, PA, 16803,
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Keywords:
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Association rules analysis ;
Classification rules mining ;
Logistic regression models ;
Subset selection method
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Abstract:
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In this paper, we develop a model selection procedure via implementing association rules analysis. This is particularly critical for applications in which interactions are present. We do this by (1) identify important rules via association rule analysis (2) convert those rules to establish all potential candidate variables, and (3) apply subset selection technique to these candidate variables. The proposed framework provides a systematic process that will help researchers deal with interactions between variables that are sometimes omitted due to the complexity of involving them in the models. The model selection procedure will find the model that constitutes the best combination of all main factors and potential interactions in terms of fit. Therefore, with the presence of interactions, this framework will provide a model with a better fit than do models developed in a classical way.
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