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Bayesian ROC curve estimation under binormality using an ordinal category likelihood

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Indexed by:期刊论文

Date of Publication:2018-01-01

Journal:COMMUNICATIONS IN STATISTICS-THEORY AND METHODS

Included Journals:SCIE

Volume:47

Issue:18

Page Number:4628-4640

ISSN No.:0361-0926

Key Words:Binormal model; Metropolis-Hastings algorithm; ordinal category likelihood; posterior consistency; ROC curve

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.

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