32 citations · 67 across the 29 of their papers we have counts for
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Applications of multivariate quasi-random sampling with neural networks
Marius Hofert, Avinash Prasad, Mu Zhu
Generative moment matching networks (GMMNs) are suggested for modeling the cross-sectional dependence between stochastic processes. The stochastic processes considered are geometri…
Matrix compatibility and correlation mixture representation of generalized Gini's gamma
Takaaki Koike, Marius Hofert
Representations of measures of concordance in terms of Pearson' s correlation coefficient are studied. All transforms of random variables are characterized such that the correlatio…
Right-truncated Archimedean and related copulas
Marius Hofert
The copulas of random vectors with standard uniform univariate margins truncated from the right are considered and a general formula for such right-truncated conditional copulas is…
Comparison of correlation-based measures of concordance in terms of asymptotic variance
Takaaki Koike, Marius Hofert
We compare measures of concordance that arise as Pearson's linear correlation coefficient between two random variables transformed so that they follow the so-called concordance-ind…
Modality for Scenario Analysis and Maximum Likelihood Allocation
Takaaki Koike, Marius Hofert
We study the variability of a risk from the statistical viewpoint of multimodality of the conditional loss distribution given that the aggregate loss equals an exogenously provided…
Outer power transformations of hierarchical Archimedean copulas: Construction, sampling and estimation
Jan Górecki, Marius Hofert, Ostap Okhrin
A large number of commonly used parametric Archimedean copula (AC) families are restricted to a single parameter, connected to a concordance measure such as Kendall's tau. This oft…
Random number generators produce collisions: Why, how many and more
Marius Hofert
It seems surprising that when applying widely used random number generators to generate one million random numbers on modern architectures, one obtains, on average, about 116 colli…
Multivariate time-series modeling with generative neural networks
Marius Hofert, Avinash Prasad, Mu Zhu
Generative moment matching networks (GMMNs) are introduced as dependence models for the joint innovation distribution of multivariate time series (MTS). Following the popular copul…