Stanislav is Associate Professor at the department of Statistical Sciences at the University of Toronto. Before joining Toronto, he was faculty at the Statistics department at Cornell and a visiting scholar at the University of Illinois at Urbana Champaign. He obtained his Phd in Mathematics from Ruhr University Bochum.
His research interests span a wide range of topics in mathematical Statistics, including empirical process theory, time series analysis, bootstrap and extreme value theory.
Talk: Structure learning for extremal graphical models
Abstract: Extremal graphical models are sparse statistical models for multivariate extreme events. The underlying graph encodes conditional independencies and enables a visual interpretation of the complex extremal dependence structure. In this talk we present a data-driven methodology to learn the underlying graph. For tree models and general extreme-value distributions, we show that the tree can be learned in a completely non-parametric fashion. For the specific class of Hüsler-Reiss distributions, we discuss methodologies for estimating general graphs. Conditions that ensure consistent graph recovery in growing dimensions are provided.