Bayesian ROC curve estimation under binormality using an ordinal category likelihood

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.

KEYWORDS:

• Binormal model
• Metropolis–Hastings algorithm
• ordinal category likelihood
• posterior consistency
• ROC curve.

MATHEMATICS SUBJECT CLASSIFICATION:

• 62F15
• 62G05

Additional information

Funding

The research work was supported by the National Natural Sciences Foundation of China [grant number 11471065], [grant number 11401072] and the Fundamental Research Funds for the Central Universities in China [grant number DUT15LK28].