Three-group ROC predictive analysis for ordinal outcomes

Abstract

The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. 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.

Non-parametric Predictive Inference for the Validation of Credit Rating Systems

“…. As the A ( n ) ‐assumption is only suitable for real‐valued data, a latent variable representation has been used for inference about an ordinal random quantity, similarly to the method that was presented by Coolen‐Maturi () for the case G =3. Thus in this section we generalize the results in Coolen‐Maturi () for more than three groups, using the idea of latent variables representations.…”

“…() by introducing NPI for three‐group ROC analysis, with real‐valued observations, to assess the ability of a diagnostic test to discriminate between three ordered classes or groups. Coolen‐Maturi () generalized the results by Elkhafifi and Coolen () by proposing NPI for three‐group ROC analysis with ordinal outcomes. This paper generalizes the methods by Coolen‐Maturi () for more than three ordered classes or groups.…”

“…Coolen‐Maturi () generalized the results by Elkhafifi and Coolen () by proposing NPI for three‐group ROC analysis with ordinal outcomes. This paper generalizes the methods by Coolen‐Maturi () for more than three ordered classes or groups. The aim of this paper is to introduce NPI for ROC curve analysis within a banking context, in particular to assess the discriminatory power of a credit rating or credit scoring system referring to its ex ante ability to distinguish between multiple ordered classes of borrowers.…”

Predictive inference for best linear combination of biomarkers subject to limits of detection

“…Recently, [24] generalized the results in [23] by introducing NPI for three-group ROC analysis, with real-valued observations, to assess the ability of a diagnostic test to discriminate among three ordered classes or groups. Coolen-Maturi [25] generalized the results in [22] by proposing NPI for three-group ROC analysis with ordinal outcomes. Below we give a brief overview of NPI for two-group ROC analysis following [23].…”

Nonparametric predictive inference with parametric copulas for combining bivariate diagnostic tests

“…NPI has also been presented for three-group ROC surfaces, with real-valued observations [9] and with ordinal observations [5], to assess the ability of a diagnostic test to discriminate among three ordered classes or groups.…”