Bayesian ROC curve estimation under binormality using an ordinal category likelihood. Issue 18 (17th September 2018)

• Record Type: Journal Article
• Authors: Wang, Xiaoguang; Niu, Yi; Li, Xiaofang
• Is Part Of: Communications in statistics. Volume 47:Issue 18(2018)
• Page Start: 4628
• Page End: 4640
• Publication Date: 2018-09-17
• Subjects: Binormal model -- Metropolis–Hastings algorithm -- ordinal category likelihood -- posterior consistency -- ROC curve.; 62F15 -- 62G05; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2017.1380830
• Languages: English
• ISSNs: 0361-0926

Abstract

ABSTRACT: Receiver operating characteristic (ROC) curve has been widely used in medical diagnosis. Various methods are proposed to estimate ROC curve parameters under the binormal model. In this paper, we propose a Bayesian estimation method from the continuously distributed data which is constituted by the truth-state-runs in the rank-ordered data. By using an ordinal category data likelihood and following the Metropolis–Hastings (M–H) procedure, we compute the posterior distribution of the binormal parameters, as well as the group boundaries parameters. Simulation studies and real data analysis are conducted to evaluate our Bayesian estimation method.