Wednesday, October 14, 2009
04:30 PM in REC 315
Assistant Professor Sergey Kirshner
Department of Statistics, Purdue University

Copulas for Learning from High-Dimensional Data

Abstract

In many applications, data sets consist of multivariate real-valued observations (e.g., financial time-series, atmospheric observations). Whether the task is to build a generative model for such data or to estimate the dependence between variables, one of the common problems is that the functional form of the dependence is not known. Copulas, multivariate distributions with uniform on [0,1] marginals, provide a flexible framework for dealing with such problems.

In the seminar, I will give a brief overview of my research while focusing on several projects related to copulas. The applications range from separation of speech signals to modeling of rainfall time series to characterization of droughts.