Multivariate Goodness-of-Fit Tests Based on Wasserstein Distance
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- Hallin, Marc & Mordant, Gilles & Segers, Johan, 2020. "Multivariate Goodness-of-Fit Tests Based on Wasserstein Distance," LIDAM Discussion Papers ISBA 2020006, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
- Hallin, Marc & Mordant, Gilles & Segers, Johan, 2021. "Multivariate Goodness-of-Fit Tests Based on Wasserstein Distance," LIDAM Reprints ISBA 2021005, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
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Cited by:
- Solveig Flaig & Gero Junike, 2022. "Scenario Generation for Market Risk Models Using Generative Neural Networks," Risks, MDPI, vol. 10(11), pages 1-28, October.
- Marc Hallin & H Lui & Thomas Verdebout, 2022. "Nonparametric Measure-transportation-based Methods for Directional Data," Working Papers ECARES 2022-18, ULB -- Universite Libre de Bruxelles.
- Hongjian Shi & Marc Hallin & Mathias Drton & Fang Han, 2020. "Rate-Optimality of Consistent Distribution-Free Tests of Independence Based on Center-Outward Ranks and Signs," Working Papers ECARES 2020-23, ULB -- Universite Libre de Bruxelles.
- Chen, Feifei & Jiménez–Gamero, M. Dolores & Meintanis, Simos & Zhu, Lixing, 2022. "A general Monte Carlo method for multivariate goodness–of–fit testing applied to elliptical families," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
- Solveig Flaig & Gero Junike, 2021. "Scenario generation for market risk models using generative neural networks," Papers 2109.10072, arXiv.org, revised Aug 2023.
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Keywords
Copula; Elliptical distribution; Goodness-of- t; Multivariate normality; Optimal transport; Semi-discrete problem; Skew-t distribution; Wasserstein distance;All these keywords.
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2020-03-23 (Econometrics)
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