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Binary classification using multivariate receiver operating characteristic curve for continuous data.
Sameera, G; Vardhan, R Vishnu; Sarma, K V S.
Affiliation
  • Sameera G; a Department of Statistics , Pondicherry University , Pondicherry , India.
  • Vardhan RV; a Department of Statistics , Pondicherry University , Pondicherry , India.
  • Sarma KV; b Department of Statistics , Sri Venkateswara University , Tirupati , India.
J Biopharm Stat ; 26(3): 421-31, 2016.
Article in En | MEDLINE | ID: mdl-26010331
ABSTRACT
The classification scenario needs handling of more than one biomarker. The main objective of the work is to propose a multivariate receiver operating characteristic (MROC) model which linearly combines the markers to classify them into one of the two groups and also to determine an optimal cut point. Simulation studies are conducted for four sets of mean vectors and covariance matrices and a real dataset is also used to demonstrate the proposed model. Linear and quadratic discriminant analysis has also been applied to the above datasets in order to explain the ease of the proposed model. Bootstrapped estimates of the parameters of the ROC curve are also estimated.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Biomarkers / ROC Curve Type of study: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: J Biopharm Stat Journal subject: FARMACOLOGIA Year: 2016 Document type: Article Affiliation country: India

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Biomarkers / ROC Curve Type of study: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: J Biopharm Stat Journal subject: FARMACOLOGIA Year: 2016 Document type: Article Affiliation country: India
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