This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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81
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Type:
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Contributed
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Date/Time:
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Sunday, August 1, 2010 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #306390 |
Title:
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Model Selection for Dependent Data via the Minimum Description Length Principle
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Author(s):
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Li Li*+ and Radu Craiu and Fang Yao
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Companies:
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University of Toronto and University of Toronto and University of Toronto
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Address:
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Department of Statistics, Toronto, ON, M5S 3G3, Canada
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Keywords:
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Dependent Data ;
Minimum Description Length Principle ;
Linear Mixed Model ;
Functional Data Analysis
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Abstract:
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We propose using minimum description length (MDL) principle to select from a class of models for dependent data. Theoretical derivations and justifications of the MDL criterion are explored for linear mixed models and for functional data models in which the within-subject correlation is taken into account. The simulation results illustrate that the method is effective in choosing the correct model in both instances.
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