3 papers
stat.ML2018
Quasi-random sampling for multivariate distributions via generative neural networks
Marius Hofert, Avinash Prasad, Mu Zhu
Generative moment matching networks (GMMNs) are introduced for generating quasi-random samples from multivariate models with any underlying copula in order to compute estimates und…
stat.ME2018
A framework for measuring dependence between random vectors
Marius Hofert, Wayne Oldford, Avinash Prasad +1
A framework for quantifying dependence between random vectors is introduced. With the notion of a collapsing function, random vectors are summarized by single random variables, cal…
math.PR2017
Hierarchical Archimax copulas
Marius Hofert, Raphael Huser, Avinash Prasad
The class of Archimax copulas is generalized to hierarchical Archimax copulas in two ways. First, a hierarchical construction of -norm generators is introduced to construct hier…