Abstract Details
Activity Number:
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658
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Type:
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Invited
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Date/Time:
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Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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ENAR
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Abstract - #307108 |
Title:
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Ultrahigh Dimensional Time Course Feature Selection
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Author(s):
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Yi Li*+ and Peirong Xu and Lixing Zhu
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Companies:
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University of Michigan and East China Normal University and Hong Kong Baptist University
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Keywords:
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time course data ;
longitudinal analysis ;
generalized estimating equations ;
variable selection ;
sure screening property
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Abstract:
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Statistical challenges arise from modern biomedical studies that produce time course genomic data with ultrahigh dimensions. For example, in a renal cancer study, the pharmacokinetic measures of a tumor suppressor (CCI-779) and expression levels of 12,625 genes were measured for each of 39 patients on 3 scheduled time points, with the goal of identifying predictive genes for pharmacokinetics over the time course. The resulting dataset defies analysis even with regularized regression. We propose a novel GEE-based screening procedure that only pertains to the specifications of the first two marginal moments and a working correlation structure. Instead of fitting separate marginal models as often adopted by the existing methods, our new procedure effectively reduces dimensionality of covariates by merely making a single evaluation of GEE functions. The new procedure is robust with respect to the mis-specification of correlation and enjoys theoretical readiness, which is further verified via intensive Monte Carlo simulations. We also apply the procedure to analyze the aforementioned renal cancer study.
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Authors who are presenting talks have a * after their name.
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