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Three-group ROC predictive analysis for ordinal outcomes.

Coolen-Maturi, T. (2016) 'Three-group ROC predictive analysis for ordinal outcomes.', Communications in statistics : theory and methods., 46 (19). 9476--9493.

Abstract

Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine, machine learning and credit scoring. The receiver operating characteristic (ROC) surface is a useful tool to assess the ability of a diagnostic test to discriminate among three ordered classes or groups. In this paper, nonparametric predictive inference (NPI) for three-group ROC analysis for ordinal outcomes is presented. NPI is a frequentist statistical method that is explicitly aimed at using few modelling assumptions, enabled through the use of lower and upper probabilities to quantify uncertainty. This paper also includes results on the volumes under the ROC surfaces and consideration of the choice of decision thresholds for the diagnosis. Two examples are provided to illustrate our method.

Item Type:Article
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1080/03610926.2016.1212074
Publisher statement:This is an Accepted Manuscript of an article published by Taylor & Francis Group in Communications in statistics : theory and methods on 12/09/2016, available online at: http://www.tandfonline.com/10.1080/03610926.2016.1212074.
Record Created:22 Jul 2016 13:51
Last Modified:17 Sep 2017 00:48

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