Statistics Seminar: Scott Scisson (UNSW) -- Symbolic data analysis for extremes

Abstract: 

Symbolic data analysis (SDA) is an underdeveloped statistical method in which the 
individual "data points" for analysis are themselves distributions. How to construct 
and then analyse these "symbols" is an ongoing research problem. This talk will 
briefly introduce the ideas behind SDA, before demonstrating how they can be used to 
fit extreme value distributions with an arbitrarily large numbers of classical 
observations. This procedure can be shown to offer one way to fit max-stable process 
models using pairwise (or higher) composite-likelihoods with an arbitrarily large 
number of spatial locations, which would otherwise be computationally prohibitive. 

(Note: This talk was recently presented at an extreme value theory workshop, hence 
the focus on extremes. The methods and ideas are broadly applicable however).

*** Note the change of location to Carslaw 273 ***