Sequential Truncation of R-Vine Copula Mixture Model for High-Dimensional Datasets

Abstract

Uncovering hidden mixture dependencies among variables has been investigated in the literature using mixture R-vine copula models. They provide considerable flexibility for modeling multivariate data. As the dimensions increase, the number of the model parameters that need to be estimated is increased dramatically, which comes along with massive computational times and efforts. This situation becomes even much more complex and complicated in the regular vine copula mixture models. Incorporating the truncation method with a mixture of regular vine models will reduce the computation difficulty for the mixture-based models. In this paper, the tree-by-tree estimation mixture model is joined with the truncation method to reduce computational time and the number of parameters that need to be estimated in the mixture vine copula models. A simulation study and real data applications illustrated the performance of the method. In addition, the real data applications show the effect of the mixture components on the truncation level.

Truncating Regular Vine Copula Based on Mutual Information: An Efficient Parsimonious Model for High-Dimensional Data

“…e data are available in R package [29] and known as "daxreturn." Also, for a comparison reason, we investigate the performance of our new approach over the sequential truncation method of [11] as its widely used in the literature [13,16]. Applying our method to this data is because vine copulas are commonly applied in finance areas.…”

“…Moreover, the sequential process of their method prevents an efficient investigation of the research space of the truncated level and converges to local optimal instead of global optimal [12]. Alanazi [13] incorporates the sequential truncation method of [11] with mixture R-vine copula models. e author shows that their method results in a dramatic reduction in the computational complexity of the R-vine copula mixture model.…”

Novel pruning and truncating of the mixture of vine copula clustering models

“…Hence, the truncation level is estimated based on the cluster data instead of the fixed prior truncation level. Alanazi 10 incorporate the truncation method of 11 , using selection criteria such as Akaike Information Criteria (AIC) of 12 ) and Bayesian Information Criteria (BIC) of 13 , into the R-vine copula mixture models, where the bivariate copulas are the mixture components. However, in the mixture of R-vine densities, the R-vine densities are the mixture components (this paper).…”