JSM 2004 Online Program

Abstract #300174

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Activity Number:	62
Type:	Invited
Date/Time:	Monday, August 9, 2004 : 8:30 AM to 10:20 AM
Sponsor:	ENAR
Abstract - #300174
Title:	Estimation and Prediction in High Dimension Low Sample Size Settings with Censored Outcomes
Author(s):	Brent A. Johnson*+ and Danyu Lin and J. Stephen Marron
Companies:	University of North Carolina and University of North Carolina and University of North Carolina
Address:	Dept. of Statistics, Raleigh, NC, 27695-001,
Keywords:	microarray ; survival analysis
Abstract:	Support Vector Machines and Distance Weighted Discrimination are two useful methods for discriminant analyses in High Dimension Low Sample Size settings. Classification becomes more difficult when the outcome is censored, however. We first explore various methods that extend SVM and DWD for predicting survival at a particular point in time. Second, we consider a penalized likelihood approach for inferring relationships between survival time and a large vector of predictors. We illustrate the methods with microarray data from two breast cancer studies.

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