Bayesian Nonparametric Models for Multiple Raters: A General Statistical Framework.

Rating procedure is crucial in many applied fields (e.g., educational, clinical, emergency). In these contexts, a rater (e.g., teacher, doctor) scores a subject (e.g., student, doctor) on a rating scale. Given raters' variability, several statistical methods have been proposed for assessing and improving the quality of ratings. The analysis and the estimate of the Intraclass Correlation Coefficient (ICC) are major concerns in such cases. As evidenced by the literature, ICC might differ across different subgroups of raters and might be affected by contextual factors and subject heterogeneity. Model estimation in the presence of heterogeneity has been one of the recent challenges in this research line. Consequently, several methods have been proposed to address this issue under a parametric multilevel modelling framework, in which strong distributional assumptions are made. We propose a more flexible model under the Bayesian nonparametric (BNP) framework, in which most of those assumptions are relaxed. By eliciting hierarchical discrete nonparametric priors, the model accommodates clusters among raters and subjects, naturally accounts for heterogeneity, and improves estimates' accuracy. We propose a general BNP heteroscedastic framework to analyze continuous and coarse rating data and possible latent differences among subjects and raters. The estimated densities are used to make inferences about the rating process and the quality of the ratings. By exploiting a stick-breaking representation of the discrete nonparametric priors, a general class of ICC indices might be derived for these models. Our method allows us to independently identify latent similarities between subjects and raters and can be applied in