Alanazi, F. Truncation Mixture R-Vine Copulas. Preprints2021, 2021020458. https://doi.org/10.20944/preprints202102.0458.v1
Alanazi, F. (2021). Truncation Mixture R-Vine Copulas. Preprints. https://doi.org/10.20944/preprints202102.0458.v1
Alanazi, F. 2021 "Truncation Mixture R-Vine Copulas" Preprints. https://doi.org/10.20944/preprints202102.0458.v1
Uncovering hidden mixture correlation among variables have been investigating in the literature using mixture R-vine copula models. These models are hierarchical in nature. They provides a huge flexibility for modelling multivariate data. As the dimensions increases, the number of the model parameters that need to be estimated is increased dramatically, which becomes along with huge computational times and efforts. This situation becomes even much more harder and complicated in the mixture Regular vine models. Incorporating truncation method with mixture Regular vine models will reduce the computation difficulty for the mixture based models. In this paper, tree-by-tree estimation mixture model is joined with the truncation method, in order to reduce the computational time and the number of the parameters that need to be estimated in the mixture vine copula models. A simulation study and a real data applications illustrated the performance of the method. In addition, the real data applications show the affect of the mixture components on the truncation level.
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