1 citations · 1 across the 7 of their papers we have counts for
7 papers
Dependence model assessment and selection with DecoupleNets
Marius Hofert, Avinash Prasad, Mu Zhu
Neural networks are suggested for learning a map from -dimensional samples with any underlying dependence structure to multivariate uniformity in dimensions. This map, term…
RafterNet: Probabilistic predictions in multi-response regression
Marius Hofert, Avinash Prasad, Mu Zhu
A fully nonparametric approach for making probabilistic predictions in multi-response regression problems is introduced. Random forests are used as marginal models for each respons…
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…
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…
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…
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…