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Statistics Seminar Series

Wednesday, Jan. 30, 2013, 2:30 PM
Cullimore, Room 611
New Jersey Institute of Technology

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Semiparametric ROC Curve Analysis under Density Ratio Models

 

Biao Zhang 

Department of Mathematics and Statistics, University of Toledo

 

Abstract

 

Receiver operating characteristic (ROC) curves are commonly used to measure the accuracy of diagnostic tests in discriminating disease and nondisease. In this talk, we discuss semiparametric statistical inferences for ROC curves under a density ratio model for disease and nondisease densities. This model has a natural connection to the logistic regression model. We explore semiparametric inference procedures for estimation of a ROC curve and the area under the ROC curve (AUC), comparison of the accuracy of two diagnostic tests, and best combination of multiple diagnostic tests. We demonstrate that statistical inferences based on a semiparametric density ratio model are more robust than a fully parametric approach and are more efficient than a fully nonparametric approach.