Date: 2019-10-04

Time: 16:00-17:00

Location: CRM, UdeM, Pav. André-Aisenstadt, 2920, ch. de la Tour, salle 1355

Abstract:

Modeling dependence between random variables is omnipresent in statistics. When rare events with high impact are involved, such as severe storms, floods or heat waves, the issue is both of great importance for risk management and theoretically challenging. Combining extreme-value theory with copula modeling and rank-based inference yields a particularly flexible and promising approach to this problem. I will present three recent advances in this area. One will tackle the question of how to account for dependence between rare events in the medium regime, in which asymptotic extreme-value models are not suitable. The other will explore what can be done when a large number of variables is involved and how a hierarchical model structure can be learned from large-scale rank correlation matrices. Finally, I won’t resist giving you a glimpse of the notoriously intricate world of rank-based inference for discrete or mixed data.

Speaker

Professor Johanna Nešlehová studied mathematics and statistics in Czechia (Univerzita Karlova, 1999) and Germany (Universität Hamburg, 2000; Carl von Ossietzky Universität, PhD, 2004). Her interests in multivariate analysis, nonparametric statistics and applications were stimulated by Marie Hušková, Georg Neuhaus and Dietmar Pfeifer. At ETH Zürich, where she was postdoc and later Heinz Hopf Lecturer, her expertise expanded to extreme-value theory and quantitative risk management under the guidance of Paul Embrechts. She joined McGill in 2009, was promoted to Associate Professor in 2012, and is currently Chair of the Undergraduate Programs in Mathematics and Statistics at that institution.