Given a multivariate compactly supported distribution φ, we derive here a necessary and sufficient condition for the global linear independence of its integer translates. This condition is based on the location of the zeros of φ̂ = The Fourier-Laplace transform of φ. The utility of the condition is demonstrated by several examples and applications, showing in particular, that previous results on...

In this paper, we propose finite mixtures of multivariate skew Laplace distributions to model both skewness and heavy-tailedness in the heterogeneous data sets. The maximum likelihood estimators for the parameters of interest are obtained by using the EM algorithm. We give a small simulation study and a real data example to illustrate the performance of the proposed mixture model.

We develop an observation that a simulation method introduced recently for heavy-tailed stochastic simulation, namely hazard-rate twisting, is equivalent to doing exponential twisting on a transformed version of the heavy-tailed random-variable; the transforming function is the hazard function. Using this approach, the paper develops efficient methods for computing portfolio value-at-risk (VAR)...

‎The goal of this study is to introduce an Asymmetric Uniform-Laplace (AUL) distribution‎. ‎We present a detailed theoretical description of this distribution‎. ‎We try to estimate the parameters of AUL distribution using the maximum likelihood method‎. ‎Since the likelihood approach results in complicated forms‎, ‎we suggest a bootstrap-based approach for es...

In order to study copula families that have different tail patterns and tail asymmetry than multivariate Gaussian and t copulas, we introduce the concepts of tail order and tail order functions. These provide an integrated way to study both tail dependence and intermediate tail dependence. Some fundamental properties of tail order and tail order functions are obtained. For the multivariate Arch...

Bayesian regularized composite quantile regression (CQR) method with group bridge penalty is adopted to conduct covariate selection and estimation in CQR. MCMC algorithm was improved for posterior inference employing a scale mixture of normal the asymmetric Laplace distribution (ALD). The suggested uses priors coefficients regression, which are mixtures multivariate uniform distributions partic...

Over the last decade there has been a marked interest to a Laplace distribution and its properties and generalizations, especially in a framework of financial applications. Such an interest has led to a revision and discussion of available goodness-of-fit procedures for a Laplace distribution. Indeed, since most of the studies which employ the Laplace distribution are concerned with modelling h...