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Found 8 papers, 2 papers with code
Dependence model assessment and selection with DecoupleNets
This map, termed DecoupleNet, is used for dependence model assessment and selection.
RafterNet: Probabilistic predictions in multi-response regression
A fully nonparametric approach for making probabilistic predictions in multi-response regression problems is introduced.
Applications of multivariate quasi-random sampling with neural networks
Generative moment matching networks (GMMNs) are suggested for modeling the cross-sectional dependence between stochastic processes.
Modality for Scenario Analysis and Maximum Likelihood Allocation
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.
Multivariate time-series modeling with generative neural networks
Generative moment matching networks (GMMNs) are introduced as dependence models for the joint innovation distribution of multivariate time series (MTS).
Markov Chain Monte Carlo Methods for Estimating Systemic Risk Allocations
We propose a novel framework of estimating systemic risk measures and risk allocations based on Markov chain Monte Carlo (MCMC) methods.
Quasi-random sampling for multivariate distributions via generative neural networks
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.
Estimators for Archimedean copulas in high dimensions
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
