Highest-density regions

Anomaly Detection in Multivariate Profiles with Conformal Bayesian Inference (Poster @ COPA2024)

This work addresses the problem of detecting anomalies or abnormal values in multivariate longitudinal data. Specifically, motivated by the international mission of antidoping agencies, we are interested in identifying potential doping abuse in …

Alternative Approaches for Estimating Highest-Density Regions

Among the variety of statistical intervals, highest-density regions (HDRs) stand out for their ability to effectively summarise a distribution or sample, unveiling its distinctive and salient features. An HDR represents the minimum size set that …

A Multivariate Copula-based Conformal Bayesian Framework for Doping Detection (Talk @ AUEB-SAW2024)

Doping control is an essential component of anti-doping organizations for protecting clean sport competitions. Since 2009, this mission has been complemented worldwide by the Athlete Biological Passport (ABP), used to monitor athletes' individual …

Probabilistic and Distance-based Approaches for Computing Highest-Density Regions (Talk @ EcoSta2023)

Many statistical problems require estimating a density function, say f , from data samples. Multivariate highest-density regions (HDRs) are considered - i.e., minimum volume sets containing a given probability - typically computed using a density …

Computing Highest Density Regions with Copulae (Poster @ 13 GdR MEMOTEF)

We investigate the problem of deriving highest density regions (HDRs) from multivariate data samples. We are interested in estimating minimum volume sets that contain a given probability. In the case of unknown distribution probabilities f, the …

Computing Highest Density Regions with Copulae (Talk @ SIS 2023)

We investigate the problem of deriving highest density regions (HDRs) from multivariate data samples. We are interested in estimating minimum volume sets that contain a given probability. In the case of unknown distribution probabilities f, the …