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Isotonic Distributional Regression (IDR) - leveraging monotonicity, uniquely so!
Virtualconference
Authors : Gneiting, Tilmann (Author of the conference)
CIRM (Publisher )
62J02 - General nonlinear regression
68T09 - Computational aspects of data analysis and big data
Additional resources :
https://www.cirm-math.fr/RepOrga/2146/Slides/GneitingTilman.pdf
CIRM (Publisher )
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Abstract :
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
MSC Codes : 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
62J02 - General nonlinear regression
68T09 - Computational aspects of data analysis and big data
Additional resources :
https://www.cirm-math.fr/RepOrga/2146/Slides/GneitingTilman.pdf
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Information on the Video
Film maker : Hennenfent, GuillaumeLanguage : English Available date : 15/06/2020 Conference Date : 02/06/2020 Subseries : Research talks arXiv category : Machine Learning ; Computer Science Mathematical Area(s) : Mathematics in Science & Technology ; Probability & Statistics Format : MP4 (.mp4) - HD Video Time : 01:01:17 Targeted Audience : Researchers Download : https://videos.cirm-math.fr/2020-06-02_Gneiting.mp4 
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Information on the Event
Event Title : Mathematical Methods of Modern Statistics 2 / Méthodes mathématiques en statistiques modernes 2Event Organizers : Bogdan, Malgorzata ; Graczyk, Piotr ; Panloup, Fabien ; Proïa, Frédéric ; Roquain, Etienne Dates : 15/06/2020 - 19/06/2020 Event Year : 2020 Event URL : 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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Bibliography
- 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
