Motivated by the construction of the often analytically tractable
Farlie...
Neural networks are suggested for learning a map from d-dimensional samp...
A fully nonparametric approach for making probabilistic predictions in
m...
In many stochastic problems, the output of interest depends on an input
...
The smooth bootstrap for estimating copula functionals in small samples ...
A new class of measures of bivariate tail dependence is proposed, which ...
Generative moment matching networks (GMMNs) are suggested for modeling t...
Representations of measures of concordance in terms of Pearson's correla...
We address the problem of estimating and comparing transformed rank
corr...
The copulas of random vectors with standard uniform univariate margins
t...
A large number of commonly used parametric Archimedean copula (AC) famil...
It seems surprising that when applying widely used random number generat...
It seems surprising that when generating one million random numbers on m...
Generative moment matching networks (GMMNs) are introduced as dependence...
Efficient computation of the distribution and log-density function of
mu...
Generative moment matching networks are introduced as quasi-random numbe...
Necessary and sufficient conditions are derived under which concordance
...
A framework for quantifying dependence between random vectors is introdu...