Agenda

Speaker: Roberta Pappadà, Università di Trieste

The talk will also be available via Zoom https://unive.zoom.us/j/85153268624
Meeting ID: 851 5326 8624
Passcode: SanMarco2

Abstract:
In recent years, copula-based measures of association have been exploited to develop clustering methods that can be used to identify the co-movements of random variables representing, e.g., a set of physical variables describing the phenomenon of interest (such as flood peak and volume). When the phenomenon under consideration is described by multiple time series collected at some given geographical sites, such clustering methods may allow the identification of sub-regions characterized by a similar stochastic behavior. While many studies have focused on a single variable of interest, the copula approach represents a natural way to develop a multivariate framework, which can consider the role of compound events for extremes. Hence, the study of compound events and the associated risk may benefit from a copula-based spatial clustering of time series. In this regard, we propose a dissimilarity-based clustering procedure to identify spatial clusters of gauge stations, each characterized by multiple time series. In particular, the procedure tends to cluster sites that exhibit a weak form of comonotonic behavior, which is more apt for some applications, thus allowing for a much more flexible notion of comonotonicity. Different dissimilarity indices are proposed, which only depend on the copula of the involved random variables, and compared in a simulation study. The proposed method is illustrated via an application to the analysis of flood risks.
The talk will present some results from ongoing research based on the collaboration with Fabrizio Durante (Università del Salento) and Sebastian Fuchs (University of Salzburg).

Bio Sketch:
Robertà Pappadà is an associate professor in Statistics at the Department of Economics, Business, Mathematics and Statistics “B. de Finetti”, University of Trieste. She holds a degree in PhD in Statistics from the University of Padova. Her main research interests are in the area of dependence modelling, especially by the means of copulas, with particular interest in applications in environmental sciences.