Estimating the AUC of mixture MROC curve in the presence of measurement errors

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.