分野別セミナー

アブストラクト：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.