[Statlist] Research Seminar in Statistics *FRIDAY, 4 NOVEMBER 2022* GSEM, University of Geneva

[gsem-support-instituts]

Dear All,

We are pleased to invite you to our next Research Seminar.

Looking forward to seeing you,


Organized by Professor Sebastian Engelke on behalf of the Research Center for Statistics (https://www.unige.ch/gsem/en/research/institutes/rcs/)


FRIDAY, 4 NOVEMBER 2022 at 11:15am, Uni-Mail M 5220 & ONLINE
Zoom research webinar: https://unige.zoom.us/j/92924332087?pwd=U1U1NFk4dTFCRHBMeWYrSDBQcXBiQT09
Meeting ID: 929 2433 2087
Passcode: 399192

Learning Extremal Graphical Structures in High Dimensions
Michaël LALANCETTE, Technical University of Munich, Germany
https://mic-lalancette.github.io/

ABSTRACT:
Multiple characterizations and models exist for extremal dependence, the dependence structure of multivariate data in unobserved tail regions. However, statistical inference for extremal dependence uses merely a fraction of the available data, drastically reducing the effective sample size and creating challenges even in moderate dimension. Recently introduced graphical models for multivariate extremes allow for enforced sparsity in moderate- to high-dimensional settings, reducing the effective dimension. In this work, we propose a novel, scalable method for selection of extremal graphical models that makes no assumption on the underlying graph structure, as opposed to existing approaches. It exploits existing tools for Gaussian graphical model selection such as the graphical lasso and neighborhood selection. Model selection consistency is established in sparse regimes where the dimension is allowed to be exponentially larger than the effective sample size.


Visit the website: https://www.unige.ch/gsem/en/research/seminars/rcs/