A0315
Title: A class of skew-multivariate distributions for spatial data
Authors: Pavel Krupskiy - Melbourne University (Australia) [presenting]
Abstract: A class of copula models is introduced for spatial data based on multivariate Pareto-mixture distributions. The tail properties of these models are explored, demonstrating their ability to capture both tail dependence and asymptotic independence, as well as the tail asymmetry frequently observed in real-world data. The proposed models also offer flexibility in accounting for permutation asymmetry and can effectively represent both the bulk and extreme tails of the distribution. Special cases of these models are considered with computationally tractable likelihoods, and an extensive simulation study is presented to assess the finite-sample performance of the maximum likelihood estimators. Finally, the models are applied to analyze a temperature dataset, showcasing their practical utility.
