51 citations · 83 across the 13 of their papers we have counts for
13 papers
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…
High-dimensional covariance matrix estimation using a low-rank and diagonal decomposition
Yilei Wu, Yingli Qin, Mu Zhu
We study high-dimensional covariance/precision matrix estimation under the assumption that the covariance/precision matrix can be decomposed into a low-rank component L and a diago…
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…