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Activity Number:
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326
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 8, 2006 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #305953 |
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Title:
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Penalized Cox Regression Analysis in the High-Dimensional and Low Sample Size Settings with Application to Microarray Gene Expression Data
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Author(s):
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Jiang Gui*+ and Hongzhe Li
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Companies:
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University of Pennsylvania and University of Pennsylvania
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Address:
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635 Blockley Hall, Philadelphia, PA, 19104,
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Keywords:
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LARS ; L1 penalized ; survival analysis ; microarray ; high-dimensional
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Abstract:
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New high-throughput technologies are generating many types of high-dimensional genomic and proteomic data. These data potentially can be used for predicting clinical outcomes and studying gene regulatory subnetworks and interindividual differences in responses to drugs. In practice, however, the number of independent samples is usually small compared to these high-dimensional genomic data. As a result, many standard statistical methods cannot be applied directly or perform poorly in such high-dimension and low sample size settings. In this talk, I will present L1 penalized methods for relating microarray gene expression data to censored survival outcomes. I will demonstrate and evaluate the proposed method using both simulations and applications to real datasets.
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