This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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676
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Type:
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Contributed
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Date/Time:
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Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #308180 |
Title:
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Nonparametric Approaches to ROC Regression
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Author(s):
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Sean Devlin*+ and Scott Emerson
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Companies:
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University of Washington and University of Washington
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Address:
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Department of Biostatistics, Seattle , WA, 98112,
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Keywords:
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nonparametric regression ;
ROC ;
diagnostic tests
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Abstract:
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The receiver operating characteristic (ROC) curve is a tool of particular use in disease status classification with a continuous medical test (marker). Various parametric and semi-parametric models of the ROC curve have been developed to compare the discriminatory ability of a marker across covariates. These methods have focused on either modeling the marker distributions, which induces an ROC curve, or modeling the ROC curve directly (ROC-GLM). In this talk, we present analogous nonparametric approaches to ROC regression using regression trees. We compare these approaches in a simulation study, and display the results using CARTScans.
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