Affiliations: [a] Department of Mathematics, VIT-AP University, Amaravati, India | [b] Department of Statistics, RSMS, Pondicherry University, Puducherry, India | [c] Department of Mathematics, LMNO, University of Caen, Caen, France
Correspondence:
[*]
Corresponding author: Christophe Chesneau, Department of Mathematics, LMNO, University of Caen, 14032 Caen, France. E-mail: [email protected].
Abstract: In a classification scenario, we usually come across data with and without class labels. If the class labels of individuals are unknown or masked by hidden components, the classifier rules must include the identification of the actual number of subcomponents in the data. Also, the presence of measurement errors in the data may influence the measures of the receiver operating characteristic model. In this paper, a mixture of multivariate receiver operating characteristic models is proposed to deal with multi-model patterns in the data, and a bias-corrected estimator is derived for estimating the area under the curve of the proposed model. The proposed methodology is supported by the real dataset and simulation studies.
Keywords: Multivariate ROC curve, measurement error, area under the curve
