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Activity Number:
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111
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Type:
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Contributed
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Date/Time:
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Monday, August 4, 2008 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #301314 |
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Title:
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Variable Selection in Linear Mixed Model: A New Algorithm Incorporating Investigator Preference and Nonmissingness of Data
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Author(s):
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Abu Minhajuddin*+ and Hrishikesh Chakraborty
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Companies:
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The University of Texas Southwestern Medical Center and RTI International
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Address:
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5323 Harry Hines Blvd, Dallas, TX, 75390,
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Keywords:
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Variable selection ; statistical model building ; linear mixed model ; stepwise ; forward selection ; backward elimination
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Abstract:
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Variable selection in the context of a linear model or a linear mixed model is a fundamental but often a contentious part in the applied statistical model building. However, very little on the topic is available in statistical literature. In the current article, we propose a new algorithm for variable selection in the context of a linear mixed model that considers investigator preference and data availability along with other statistical consideration. The performance of the new algorithm is contrasted with the available automated variable selection approaches like stepwise, forward selection, and backward elimination and the best subset selection using a real data set. Cross-validation method is used to assess the predictive performance of the estimated model.
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