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Abstract Details
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Activity Number:
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380
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Type:
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Invited
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Date/Time:
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Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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IMS
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| Abstract - #300011 |
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Title:
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A Unified Approach for Support Vector Regression Under Right Censoring
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Author(s):
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Yair Goldberg*+ and Michael R. Kosorok
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Companies:
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The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
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Address:
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Department of Biostatistics,, Chapel Hill, NC, 27599,
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Keywords:
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survival analysis ;
support vecotor machine ;
generalization error
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Abstract:
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We develop a unified approach for support vector machines for classification and regression in which the outcomes are functions of the survival times subject to right censoring. We present a novel support-vector regression algorithm that is adjusted for censored data. We provide finite sample bounds on the generalization error the algorithm. We apply the general methodology for estimation of the (truncated) mean, median, quantiles, and for classification problems.
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