Virtualconference
H Isotonic Distributional Regression (IDR) - leveraging monotonicity, uniquely so!
Auteurs : Gneiting, Tilmann (Auteur de la Conférence)
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... (Editeur )
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- GNEITING, Tilmann et RAFTERY, Adrian E. Strictly proper scoring rules, prediction, and estimation. Journal of the American statistical Association, 2007, vol. 102, no 477, p. 359-378. - https://doi.org/10.1198/016214506000001437
- GNEITING, Tilmann et KATZFUSS, Matthias. Probabilistic forecasting. Annual Review of Statistics and Its Application, 2014, vol. 1, p. 125-151. - https://doi.org/10.1146/annurev-statistics-062713-085831
- JORDAN, Alexander I., MÜHLEMANN, Anja, et ZIEGEL, Johanna F. Optimal solutions to the isotonic regression problem. arXiv preprint arXiv:1904.04761, 2019. - https://arxiv.org/abs/1904.04761
- HENZI, Alexander, ZIEGEL, Johanna F., et GNEITING, Tilmann. Isotonic Distributional Regression. arXiv preprint arXiv:1909.03725, 2019. - https://arxiv.org/abs/1909.03725
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Résumé :
There is an emerging consensus in the transdiciplinary literature that the ultimate goal of regression analysis is to model the conditional distribution of an outcome, given a set of explanatory variables or covariates. This new approach is called "distributional regression", and marks a clear break from the classical view of regression, which has focused on estimating a conditional mean or quantile only. Isotonic Distributional Regression (IDR) learns conditional distributions that are simultaneously optimal relative to comprehensive classes of relevant loss functions, subject to monotonicity constraints in terms of a partial order on the covariate space. This IDR solution is exactly computable and does not require approximations nor implementation choices, except for the selection of the partial order. Despite being an entirely generic technique, IDR is strongly competitive with state-of-the-art methods in a case study on probabilistic precipitation forecasts from a leading numerical weather prediction model.
Joint work with Alexander Henzi and Johanna F. Ziegel.
Joint work with Alexander Henzi and Johanna F. Ziegel.
Keywords : Conditional distribution estimation; continuous ranked probability score; ensemble methods; monotonicity; probabilistic forecast; proper scoring rule; stochastic order; weather prediction
Codes MSC :
62J02
- General nonlinear regression
68T09
- Computational aspects of data analysis and big data
Ressources complémentaires :
https://www.cirm-math.fr/RepOrga/2146/Slides/GneitingTilman.pdf
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Informations sur la Vidéo
Réalisateur : Hennenfent, GuillaumeLangue : Anglais Date de publication : 15/06/2020 Date de captation : 02/06/2020 Collection : Research talks ; Mathematics in Science and Technology ; Probability and Statistics Durée : 01:01:17 Domaine : Mathematics in Science & Technology ; Probability & Statistics Audience : Chercheurs ; Doctorants , Post - Doctorants Download : https://videos.cirm-math.fr/2020-06-02_Gneiting.mp4 
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Informations sur la Rencontre Virtuelle
Nom de la rencontre : Mathematical Methods of Modern Statistics 2 / Méthodes mathématiques en statistiques modernes 2Organisateurs de la rencontre : Bogdan, Malgorzata ; Graczyk, Piotr ; Panloup, Fabien ; Proïa, Frédéric ; Roquain, Etienne Dates : 15/06/2020 - 19/06/2020 Année de la rencontre : 2020 URL Congrès : https://www.cirm-math.com/cirm-virtual-...
Citation Data
DOI : 10.24350/CIRM.V.19640703Cite this video as: Gneiting, Tilmann (2020). Isotonic Distributional Regression (IDR) - leveraging monotonicity, uniquely so!.CIRM . Audiovisual resource. doi:10.24350/CIRM.V.19640703 URI : http://dx.doi.org/10.24350/CIRM.V.19640703 |
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