CIRM - Videos & books Library - Isotonic Distributional Regression (IDR)

Virtualconference

Auteurs : Gneiting, Tilmann (Auteur de la Conférence)
CIRM (Editeur )

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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.

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

Informations sur la Vidéo

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Hennenfent, Guillaume

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https://videos.cirm-math.fr/2020-06-02_Gneiting.mp4

Informations sur la Rencontre

Nom de la rencontre : Mathematical Methods of Modern Statistics 2 / Méthodes mathématiques en statistiques modernes 2
Organisateurs 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-...

Données de citation

DOI : 10.24350/CIRM.V.19640703
Citer cette vidéo: 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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Bibliographie

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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