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Activity Number: 656
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #321184 View Presentation
Title: Hypothesis Tests for Hypervolumes Under K-Dimensional ROC Manifold
Author(s): Rajarshi Dey*
Companies: University of South Alabama
Keywords: ROC manifold ; Hypervolume ; U-statistic ; Permutation-like approach

Consider any classification procedure for K groups based on a certain marker X. Let; Xi ? Fi; i=1,2,.,K where Fi is continuous for i= 1,2,.,K. The most common index to measure the performance of the marker is HUM (Hyper-volume Under Manifold) of the ROC (Receiver Operating Characteristic) manifold obtained by this marker. A random marker obtains an HUM of 1/K!. So, a natural setup for testing the performance of the marker is H0: HUM = 1/K! vs. HA: HUM > 1/K! . However, for even moderately large K; this test is useless as 1/K! is very small. I will discuss two new tests for testing the performance of a marker on a K(>2) group classification problem.

Authors who are presenting talks have a * after their name.

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