Collection Mathematical Methods of Modern Statistics 2 / Méthodes mathématiques en statistiques modernes 2

Organizer(s) Bogdan, Malgorzata ; Graczyk, Piotr ; Panloup, Fabien ; Proïa, Frédéric ; Roquain, Etienne

Date(s) 15/06/2020 - 19/06/2020

linked URL https://www.cirm-math.com/cirm-virtual-event-2146.html

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Isotonic Distributional Regression (IDR) - leveraging monotonicity, uniquely so!

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.

Information about the video

Date of recording 02/06/2020
Date of publication 15/06/2020
Institution CIRM
Licence CC BY NC ND
Language English
Audience Researchers
Director(s) Guillaume Hennenfent
Format MP4

Citation data

DOI 10.24350/CIRM.V.19640703
Cite this video Gneiting, Tilmann (02/06/2020). Isotonic Distributional Regression (IDR) - leveraging monotonicity, uniquely so!. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19640703
URL https://dx.doi.org/10.24350/CIRM.V.19640703

Domain(s)

Bibliography

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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