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A copula-based Bayesian framework for doping detection

Author

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  • Nina Deliu

    (Sapienza Università di Roma
    University of Cambridge)

  • Brunero Liseo

    (Sapienza Università di Roma)

Abstract

Doping control is an essential component of anti-doping organizations for protecting sport competitions. Since 2009, this mission has been complemented worldwide by the Athlete Biological Passport (ABP), used to monitor athletes’ individual profiles over time. The practical implementation of the ABP is based on a Bayesian framework, called ADAPTIVE, intended to identify individual reference ranges outside of which an observation may indicate doping abuse. Currently, this method follows a univariate approach, relying on simultaneous analysis of different markers. 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, we discuss our approach to inference, grounded in a Bayesian spirit, and present an extension to multidimensional predictive reference regions. Focusing on the hematological module of the ABP, we evaluate the proposed framework in both data-driven simulations and real data.

Suggested Citation

  • Nina Deliu & Brunero Liseo, 2025. "A copula-based Bayesian framework for doping detection," Computational Statistics, Springer, vol. 40(4), pages 1873-1898, April.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:4:d:10.1007_s00180-024-01579-x
    DOI: 10.1007/s00180-024-01579-x
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    References listed on IDEAS

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    1. Hobæk Haff, Ingrid & Aas, Kjersti & Frigessi, Arnoldo, 2010. "On the simplified pair-copula construction -- Simply useful or too simplistic?," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1296-1310, May.
    2. Luke O. Ouma & Michael J. Grayling & James M. S. Wason & Haiyan Zheng, 2022. "Bayesian modelling strategies for borrowing of information in randomised basket trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 2014-2037, November.
    3. Michael S. Smith & Quan Gan & Robert J. Kohn, 2012. "Modelling dependence using skew t copulas: Bayesian inference and applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(3), pages 500-522, April.
    4. Grazian, Clara & Dalla Valle, Luciana & Liseo, Brunero, 2022. "Approximate Bayesian conditional copulas," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    5. D. Concordet & R. Servien, 2014. "Individual prediction regions for multivariate longitudinal data with small samples," Biometrics, The International Biometric Society, vol. 70(3), pages 629-638, September.
    6. Xu, Ganggang & Zhu, Huirong & Lee, J. Jack, 2020. "Borrowing strength and borrowing index for Bayesian hierarchical models," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    7. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
    8. Joe, Harry, 2005. "Asymptotic efficiency of the two-stage estimation method for copula-based models," Journal of Multivariate Analysis, Elsevier, vol. 94(2), pages 401-419, June.
    9. Aleksey Min & Claudia Czado, 2010. "Bayesian Inference for Multivariate Copulas Using Pair-Copula Constructions," Journal of Financial Econometrics, Oxford University Press, vol. 8(4), pages 511-546, Fall.
    10. Irène Gijbels & Marek Omelka & Noël Veraverbeke, 2015. "Estimation of a Copula when a Covariate Affects only Marginal Distributions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1109-1126, December.
    11. Nagler, Thomas & Czado, Claudia, 2016. "Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas," Journal of Multivariate Analysis, Elsevier, vol. 151(C), pages 69-89.
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