Μεταπτυχιακές Εργασίες
Μόνιμο URI για αυτήν τη συλλογήhttps://pyxida.aueb.gr/handle/123456789/15
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Πλοήγηση Μεταπτυχιακές Εργασίες ανά Επιβλέπων / ουσα "Deliu, Nina"
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Τεκμήριο Computational approaches for Bayesian inference in copula based hierarchical models: an application to anti-doping(2026-02-11) Vyltanioti, Pigi-Eva; Βυλτανιώτη, Πηγή-Εύα; Deliu, Nina; Karlis, Dimitris; Liseo, Brunero; Ntzoufras, IoannisAnti-doping organizations invest a lot on doping control in order to protect sport competitions. On 2009, the Athlete Biological Passport (ABP) complemented this mission worldwide as it is used to monitor athlete’s individual profiles over time. It is implemented through a Bayesian framework, called ADAPTIVE, which determines individual reference ranges beyond which a measurement may signal potential doping. Doping detection increasingly relies on longitudinal biomarker monitoring, yet most current statistical tools analyze biomarkers marginally and ignore their dependence structure. This creates a methodological gap, as doping often alters multivariate patterns rather than individual values. Copula models provide a principled way to separate marginal behavior from joint dependence, making them well suited for detecting multivariate deviations. This thesis aims to develop and evaluate a Bayesian copula-mixture framework for modelling longitudinal hematological biomarkers within the context of the Athlete Biological Passport. The central research question is how different Bayesian estimation strategies affect the recovery of marginal parameters and dependence structure. The proposed methodology, full MCMC, combined flexible mixture models for marginal distributions with a survival Clayton copula to capture upper-tail dependence, a feature particularly relevant for detecting coordinated biomarker elevations. Two estimation strategies are implemented and compared, a Bayesian-IFM scheme and a full Bayesian MCMC algorithm, and extensive simulation studies are con ducted to assess identifiability, bias, RMSE, posterior uncertainty and and predictive performance under each approach. Results show that the full MCMC method improves the recovery of the copula parameter and better captures tail dependence, while the joint sampling approach offers greater numerical stability and more precise marginal estimates. The comparison highlights clear trade-offs between computational efficiency, dependence estimation accuracy and uncertainty quantification, providing practical guidance for Bayesian dependence modeling in anti-doping applications.