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Abstract Details
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Activity Number:
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525
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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International Chinese Statistical Association
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| Abstract - #306922 |
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Title:
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On Variable Selection for Large-Dimensional Categorical Data Analysis
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Author(s):
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Philip E. Cheng*+ and Keng-Min Lin and Michelle Liou and Ben-Chang Shia
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Companies:
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Academia Sinica and Academia Sinica and Academia Sinica and Fu-Jen Catholic University
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Address:
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Institute of Statistical Science, , , Republic of China
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Keywords:
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Categorical Data ;
Model Selection ;
Mutual Information ;
Variable Selection
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Abstract:
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Recent studies by the authors (Statistica Sinica, 2008; JASA, 2010) presented the use of information geometry for categorical data analysis. It also introduces concise methods of selecting explanatory variables for a response variable of interest in the analysis of large and sparse contingency tables. Methods of variable selection are derived from identifying significant mutual information statistics between the variables and the response variable, prior to using model selection criteria with general linear models. The proposed variable selection methods are examined using a few large contingency data tables, and compared to the usual analysis of variable selection and model selection in using general linear models.
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