In this paper we take up Bayesian inference in general multivariate stable distributions. We exploit the representation of Matsui and Takemura (2009) for univariate projections, and the representation of the distributions in terms of their spectral measure. We present e cient MCMC schemes to...

In this paper we take up Bayesian inference in general, multivariate stable distributions. We use approximate Bayesian computation (ABC) along with carefully crafted proposal distributions for the implementation of MCMC. The problem of selecting summary statistics in ABC is resolved through the...

Multivariate statistical analysis uses techniques of variables selection and aggregation. Numerous economic phenomena belong to the application field of multivariate analysis. Generally all phenomena described by large number of variables may be analyzed within this framework. On the other side,...

In this article we propose a new graphical method to help us to uncover potential outliers in multivariate samples. The idea behind the method is to analyze the behavior of a growing neighborhood of each data point. This method is very robust and permits to find outliers in very complex structures

The interdependence between multiple lines of business has an important impact on determining loss reserves and risk capital, which are crucial for the solvency of a property and casualty (P&C) insurance company. In this work, we introduce the two-stage inference method using the Sarmanov family...

Topological data analysis provides a new perspective on many problems in the domain of complex systems. Here, we establish the dependency of the mean value of functional $p$-norms of 'persistence landscapes' on a uniform scaling of the underlying multivariate distribution. Furthermore, we...