Highest-Density Regions (HDRs) allow uncertainty estimation of predictions, estimates, or distributions of interest by identifying the samples with the highest density while covering the smallest possible volume. Therefore, they ensure the greatest efficiency. This paper extends the framework proposed by [3] to estimate HDRs using neighborhood measures. We generalize their approach to a multivariate setting, using both nonparametric distance-based measures and parametric measures making use of vine copulas.
Using Vine Copulas for Estimating Highest-Density Regions in Multivariate Data / Masillo, Emanuele; Deliu, Nina. - (2025), pp. 488-493. ( Scientific Meeting of the Italian Statistical Society Genova ) [10.1007/978-3-031-95995-0_81].
Using Vine Copulas for Estimating Highest-Density Regions in Multivariate Data
Deliu, NinaUltimo
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2025
Abstract
Highest-Density Regions (HDRs) allow uncertainty estimation of predictions, estimates, or distributions of interest by identifying the samples with the highest density while covering the smallest possible volume. Therefore, they ensure the greatest efficiency. This paper extends the framework proposed by [3] to estimate HDRs using neighborhood measures. We generalize their approach to a multivariate setting, using both nonparametric distance-based measures and parametric measures making use of vine copulas.| File | Dimensione | Formato | |
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