title: Bayesian inference for agreement measures.
abstract: The agreement of different measurement methods is an important issue in several disciplines like, for example, Medicine, Metrology, and Engineering. In this article, some agreement measures, common in the literature, were analyzed from a Bayesian point of view. Posterior inferences for such agreement measures were obtained based on well-known Bayesian inference procedures for the bivariate normal distribution. As a consequence, a general, simple, and effective method is presented, which does not require Markov Chain Monte Carlo methods and can be applied considering a great variety of prior distributions. Illustratively, the method was exemplified using five objective priors for the bivariate normal distribution. A tool for assessing the adequacy of the model is discussed. Results from a simulation study and an application to a real dataset are also reported.
authors: a Instituto de Matemática y Física, Universidad de Talca , Talca , Chile.
authors: Ignacio
authors: I
authors: Vidal
authors: b Universidade de São Paulo, Instituto de Ciências Matemáticas e de Computação , São Carlos , SP , Brasil.
authors: Mário
authors: M
authors: de Castro
mesh_terms: D001499:Bayes Theorem; D016208:Databases, Factual / Q000706:statistics & numerical data*; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method
keywords: Accuracy; concordance correlation coefficient; coverage probability; precision; total deviation index