| Hierarchical archimax copulas M Hofert, R Huser, A Prasad Journal of Multivariate Analysis 167, 195-211, 2018 | 21 | 2018 |
| Quasi-random sampling for multivariate distributions via generative neural networks M Hofert, A Prasad, M Zhu Journal of Computational and Graphical Statistics 30 (3), 647-670, 2021 | 11 | 2021 |
| Multivariate time-series modeling with generative neural networks M Hofert, A Prasad, M Zhu Econometrics and Statistics 23, 147-164, 2022 | 8 | 2022 |
| A framework for measuring association of random vectors via collapsed random variables M Hofert, W Oldford, A Prasad, M Zhu Journal of Multivariate Analysis 172, 5-27, 2019 | 8 | 2019 |
| Applications of multivariate quasi-random sampling with neural networks M Hofert, A Prasad, M Zhu International Conference on Monte Carlo and Quasi-Monte Carlo Methods in …, 2020 | 2 | 2020 |
| RafterNet: Probabilistic predictions in multi-response regression M Hofert, A Prasad, M Zhu The American Statistician 77 (4), 406-416, 2023 | 1 | 2023 |
| Quasi-random sampling for multivariate distributions via generative neural networks M Hofert, A Prasad, M Zhu arXiv preprint arXiv:1811.00683, 2018 | 1 | 2018 |
| A framework for measuring dependence between random vectors M Hofert, W Oldford, A Prasad, M Zhu arXiv preprint arXiv:1801.03596, 2018 | 1 | 2018 |
| Dependence Model Assessment and Selection with DecoupleNets M Hofert, A Prasad, M Zhu Journal of Computational and Graphical Statistics 32 (4), 1272-1286, 2023 | | 2023 |
| Dependence model assessment and selection with DecoupleNets M Hofert, A Prasad, M Zhu arXiv preprint arXiv:2202.03406, 2022 | | 2022 |
| Dependence: From classical copula modeling to neural networks AS Prasad University of Waterloo, 2020 | | 2020 |
| Quasi-random number generators for multivariate distributions based on generative neural networks M Hofert, A Prasad, M Zhu stat 1050, 1, 2018 | | 2018 |
| Nested and Hierarchical Archimax copulas M Hofert, R Huser, A Prasad arXiv, 2017 | | 2017 |
