no code implementations • 7 Feb 2022 • Marius Hofert, Avinash Prasad, Mu Zhu
This map, termed DecoupleNet, is used for dependence model assessment and selection.
no code implementations • 2 Dec 2021 • Marius Hofert, Avinash Prasad, Mu Zhu
A fully nonparametric approach for making probabilistic predictions in multi-response regression problems is introduced.
no code implementations • 15 Dec 2020 • Marius Hofert, Avinash Prasad, Mu Zhu
Generative moment matching networks (GMMNs) are suggested for modeling the cross-sectional dependence between stochastic processes.
no code implementations • 6 May 2020 • Takaaki Koike, Marius Hofert
We show that various distributional properties of this conditional distribution, such as modality, dependence and tail behavior, are inherited from those of the underlying joint loss distribution.
no code implementations • 25 Feb 2020 • 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).
no code implementations • 25 Sep 2019 • Takaaki Koike, Marius Hofert
We propose a novel framework of estimating systemic risk measures and risk allocations based on Markov chain Monte Carlo (MCMC) methods.
1 code implementation • 1 Nov 2018 • Marius Hofert, Avinash Prasad, Mu Zhu
Once trained on pseudo-random samples from a parametric model or on real data, these neural networks only require a multivariate standard uniform randomized QMC point set as input and are thus fast in estimating expectations of interest under dependence with variance reduction.
1 code implementation • 6 Jul 2012 • Marius Hofert, Martin Maechler, Alexander J. McNeil
The performance of known and new parametric estimators for Archimedean copulas is investigated, with special focus on large dimensions and numerical difficulties.
Computation Numerical Analysis Other Statistics 62H12, 62F10, 62H99, 62H20, 65C60