[Forum SIS] Seminario Philippe Naveau - 4 Novembre

[Ilaria Prosdocimi]

Cari colleghi e colleghe,

Con grande piacere, vi segnalo il prossimo seminario del gruppo Statistica
al DAIS, Ca' Foscari:

Data: 4 Novembre, ore 12:15-13:15
Relatore: Philippe Naveau (Laboratoire des Sciences du Climat et de
l’Environnement)
Titolo: Evaluation of binary classifiers for environmental extremes

Il seminario si potrà seguire tramite la piattaforma Zoom:

https://unive.zoom.us/j/85153268624
Meeting ID: 851 5326 8624
Passcode: SanMarco2

Il seminario si terrà nell'aula Delta 2B del Campus di Via Torino a Mestre
(VE) e sono disponibili alcuni posti nell'aula. Per chi desidera
partecipare in presenza chiediamo di registrarsi usando questo form (
https://forms.gle/7MijuYAbTkCsy4fw7): i posti a sedere sono limitati e
saranno assegnati tramite modalità "first come first served".

Abstract:
Machine learning classification methods usually assume that all possible
classes are sufficiently present within the training set. Due to their
inherent rarities, extreme events are always under-represented and
classifiers tailored for predicting extremes need to be carefully designed
to handle this under-representation. In this talk, we address the question
of how to assess and compare classifiers with respect to their capacity to
capture extreme occurrences.This is also related to the topic of scoring
rules used in forecasting literature. In this context, we propose and study
different risk functions adapted to extremal classifiers. The inferential
properties of our empirical risk estimator are derived under the framework
of multivariate regular  variation and hidden regular variation. As an
example, we study in detail the special class of linear classifiers and
show that the optimisation of our risk function leads to a consistent
solution. A simulation study compares different cla
 ssifiers and indicates their performance with respect to our risk
functions. To conclude, we apply our framework to the analysis of extreme
river discharges in the Danube river basin. The application compares
different predictive algorithms and test their capacity at forecasting
river discharges from other river stations. As a by-product, we identify
the explanatory variables that contribute the most to extremal behaviour.
If time allows, we will also discuss other climate datasets.

Joint work with Juliette Legrand (LSCE, Rennes University) and Marco
Oesting (Siegen University)


Cordiali saluti

Ilaria Prosdocimi
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