18 April 2024: Nina Deliu (Sapienza University of Rome)

PLEASE NOTE THE RECORDING MISSED THE FIRST MINUTE OF THE PRESENTATION
Title: A Multivariate Copula-based Conformal Bayesian Framework for Doping Detection
Abstract: The analytical implementation of international anti-doping programs for protecting clean sport competitions is based on a univariate Bayesian framework, called ADAPTIVE. This is intended to identify individual reference ranges outside of which an observation may indicate doping abuse, and relies on simultaneous analysis of different markers, without accounting for their relationship.
This work extends the ADAPTIVE method to a multivariate testing framework, making use of copula models to couple the marginal distribution of biomarkers with their dependence structure. After introducing the proposed copula-based hierarchical model, I will discuss our approach to inference, grounded in a Bayesian spirit, and present a conformal method for constructing predictive reference regions. As a conformal measure, we use the posterior predictive density of the multidimensional biomarkers of individual athletes. Focusing on the haematological module of the ABP, we evaluate the proposed framework in both data-driven simulations and real data.
This is a joint work with Brunero Liseo.