Sampling Archimedean copulas. - Marius Hofert (Comput. Stat. Data Anal., 2008)

Constructing hierarchical Archimedean copulas with Lévy subordinators. - Christian Hering; Marius Hofert; Jan-Frederik Mai; Matthias Scherer (J. Multivar. Anal., 2010)

Efficiently sampling nested Archimedean copulas. - Marius Hofert (Comput. Stat. Data Anal., 2011)

Sampling Exponentially Tilted Stable Distributions. - Marius Hofert (ACM Trans. Model. Comput. Simul., 2011)

Likelihood inference for Archimedean copulas in high dimensions under known margins. - Marius Hofert; Martin Mächler; Alexander J. McNeil (J. Multivar. Anal., 2012)

Densities of nested Archimedean copulas. - Marius Hofert; David Pham (J. Multivar. Anal., 2013)

Sibuya copulas. - Marius Hofert; Frédéric Vrins (J. Multivar. Anal., 2013)

A note on generalized inverses. - Paul Embrechts; Marius Hofert (Math. Methods Oper. Res., 2013)

On Sampling from the Multivariate t Distribution. - Marius Hofert (R J., 2013)

Construction and sampling of Archimedean and nested Archimedean Lévy copulas. - Oliver Grothe; Marius Hofert (J. Multivar. Anal., 2015)

An approach to structure determination and estimation of hierarchical Archimedean Copulas and its application to Bayesian classification. - Jan Górecki; Marius Hofert; Martin Holena (J. Intell. Inf. Syst., 2016)

Quasi-random numbers for copula models. - Mathieu Cambou; Marius Hofert; Christiane Lemieux (Stat. Comput., 2017)

Quasi-random number generators for multivariate distributions based on generative neural networks. - Marius Hofert; Avinash Prasad; Mu Zhu (CoRR, 2018)

Hierarchical Archimax copulas. - Marius Hofert; Raphaël Huser; Avinash Prasad (J. Multivar. Anal., 2018)

A framework for measuring association of random vectors via collapsed random variables. - Marius Hofert; Wayne Oldford; Avinash Prasad; Mu Zhu (J. Multivar. Anal., 2019)

Applications of Multivariate Quasi-Random Sampling with Neural Networks. - Marius Hofert; Avinash Prasad; Mu Zhu (MCQMC, 2020)

Multivariate time-series modeling with generative neural networks. - Marius Hofert; Avinash Prasad; Mu Zhu (CoRR, 2020)

Random number generators produce ties: Why and how many. - Marius Hofert (CoRR, 2020)

Zigzag Expanded Navigation Plots in R: The R Package zenplots. - Marius Hofert; Wayne Oldford (J. Stat. Softw., 2020)

RafterNet: Probabilistic predictions in multi-response regression. - Marius Hofert; Avinash Prasad; Mu Zhu (CoRR, 2021)

Outer power transformations of hierarchical Archimedean copulas: Construction, sampling and estimation. - Jan Górecki; Marius Hofert; Ostap Okhrin (Comput. Stat. Data Anal., 2021)

Normal variance mixtures: Distribution, density and parameter estimation. - Erik Hintz; Marius Hofert; Christiane Lemieux (Comput. Stat. Data Anal., 2021)

Quasi-Random Sampling for Multivariate Distributions via Generative Neural Networks. - Marius Hofert; Avinash Prasad; Mu Zhu (J. Comput. Graph. Stat., 2021)

Multivariate Normal Variance Mixtures in R: The R Package nvmix. - Erik Hintz; Marius Hofert; Christiane Lemieux (J. Stat. Softw., 2022)

Dependence Model Assessment and Selection with DecoupleNets. - Marius Hofert; Avinash Prasad; Mu Zhu (J. Comput. Graph. Stat., 2023)

Implicit and Explicit Policy Constraints for Offline Reinforcement Learning. - Yang Liu; Marius Hofert (CLeaR, 2024)

Policy Optimization by Looking Ahead for Model-based Offline Reinforcement Learning. - Yang Liu; Marius Hofert (ICRA, 2024)

Comparison of correlation-based measures of concordance in terms of asymptotic variance. - Takaaki Koike; Marius Hofert (J. Multivar. Anal., 2024)