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Title: Bayesian nonparametric inference for the coefficient of overlap Authors:  Javier Garrido Guillen - University of Edinburgh (United Kingdom)
Maria Xose Rodriguez-Alvarez - BCAM - Basque Center for Applied Mathematics (Spain)
Vanda Inacio - University of Edinburgh (United Kingdom) [presenting]
Abstract: Accurate diagnosis of disease is of fundamental importance in medical research and clinical practice. The major goal of a diagnostic test is to distinguish between diseased and nondiseased individuals and before a test is widely used in practice, its discriminatory ability must be rigorously assessed through statistical analysis. The overlap coefficient, which is defined as the proportion of overlap area between two density functions, has gained unarguably popularity as a summary measure of diagnostic accuracy. We propose a Bayesian nonparametric modelling framework, based on a combination of Dirichlet process mixtures and the Bayesian bootstrap, for the overlap coefficient. The performance of our methods is assessed through multiple simulation studies and an application to real data is provided.