175
Views
0
CrossRef citations to date
0
Altmetric
Machine Learning

Dependence Model Assessment and Selection with DecoupleNets

ORCID Icon, &
Pages 1272-1286 | Received 15 Feb 2022, Accepted 07 Dec 2022, Published online: 06 Feb 2023

References

  • Bonneel, N., Rabin, J., Peyré, G., and Pfister, H. (2015), “Sliced and Radon Wasserstein Barycenters of Measures,” Journal of Mathematical Imaging and Vision, 51, 22–45. DOI: 10.1007/s10851-014-0506-3.
  • Dissmann, J., Brechmann, E. C., Czado, C., and Kurowicka, D. (2013), “Selecting and Estimating Regular Vine Copulae and Application to Financial Returns,” Computational Statistics & Data Analysis, 59, 52–69. DOI: 10.1016/j.csda.2012.08.010.
  • Dziugaite, G. K., Roy, D. M., and Ghahramani, Z. (2015), “Training Generative Neural Networks via Maximum Mean Discrepancy Optimization,” in Proceedings of the Conference on Uncertainty in Artificial Intelligence. Available at http://www.auai.org/uai2015/proceedings/papers/230.pdf
  • Genest, C., Rémillard, B., and Beaudoin, D. (2009), “Goodness-of-Fit Tests for Copulas: A Review and a Power Study,” Insurance: Mathematics and Economics, 44, 199–213. DOI: 10.1016/j.insmatheco.2007.10.005.
  • Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A. (2007), “A Kernel Method for the Two-Sample-Problem,” in Advances in Neural Information Processing Systems, pp. 513–520.
  • Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A. (2012), “A Kernel Two-Sample Test,” Journal of Machine Learning Research, 13, 723–773.
  • Hofert, M., Kojadinovic, I., Mächler, M., and Yan, J. (2018), Elements of Copula Modeling with R. Springer Use R! Series. Cham: Springer.
  • Hofert, M., and Mächler, M. (2014), “A Graphical Goodness-of-Fit Test for Dependence Models in Higher Dimensions,” Journal of Computational and Graphical Statistics, 23, 700–716. DOI: 10.1080/10618600.2013.812518.
  • Hofert, M., and Oldford, R. W. (2018), “Visualizing Dependence in High-Dimensional Data: An Application to S&P 500 Constituent Data,” Econometrics and Statistics, 8, 161–183. DOI: 10.1016/j.ecosta.2017.03.007.
  • Hofert, M., Prasad, A., and Zhu, M. (2021a), “Multivariate Time-Series Modeling with Generative Neural Networks,” Econometrics and Statistics, 23, 147–164. DOI: 10.1016/j.ecosta.2021.10.011.
  • Hofert, M., Prasad, A., and Zhu, M. (2021b), “Quasi-Random Sampling for Multivariate Distributions via Generative Neural Networks,” Journal of Computational and Graphical Statistics, 30, 647–670.
  • Kingma, D. P., and Ba, J. (2014), “Adam: A Method for Stochastic Optimization,” Available at https://arxiv.org/abs/1412.6980
  • Li, Y., Swersky, K., and Zemel, R. (2015), “Generative Moment Matching Networks,” in International Conference on Machine Learning, pp. 1718–1727.
  • Munkres, J. R. (2000), Topology (2nd ed.), Upper Saddle River, NJ: Prentice Hall.
  • Rosenblatt, M. (1952), “Remarks on a Multivariate Transformation,” The Annals of Mathematical Statistics, 23, 470–472. DOI: 10.1214/aoms/1177729394.
  • Wu, F., Valdez, E. A., and Sherris, M. (2007), “Simulating Exchangeable Multivariate Archimedean Copulas and its Applications,” Communications in Statistics – Simulation and Computation, 36, 1019–1034. DOI: 10.1080/03610910701539781.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.