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Abstract Details
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Activity Number:
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359
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #301795 |
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Title:
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An Empirical Maximum Likelihood ROC Model with Monotonic Likelihood Ratio
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Author(s):
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Lucas Simplice Tcheuko*+
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Companies:
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University of Maryland
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Address:
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3554 childress terrace , Burtonsville, MD, 20866,
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Keywords:
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empirical likelihood ;
convex ;
PAVA ;
constrained estimates
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Abstract:
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Although an ideal ROC curves is expected to be convex, published fits often displays ``hooks".This article presents a method of computing an empirical likelihood estimator of the ROC assuming convexity.For a given set of binary data, we first derive its empirical ROC without convexity assumption, and then derive the analytical convex maximum likelihood estimator of the ROC. We use use PAVA -Pool Adjacent Violator Algorithm- to compute the real values of the convex ROC estimator and then overlay the convex plot on the empirical plot.
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Authors who are presenting talks have a * after their name.
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