Operations Research and Financial Engineering
Covariance estimation - big data challenges and financial applications
High-dimensional settings have recently become one of the major focuses of statistical research, driven by applications in finance and genetics. In a data rich environment, the number of parameters can diverge at a rate faster than that of the sample size. My focus is on creating consistent covariance and precision matrix estimators. I propose the POET (Principal Orthogonal complEments Thresholding) estimator which deals with the big data challenge by establishing low-dimensional patterns, namely a low-rank component and a sparse component. I demonstrate its performance in a real-data setup for risk management and portfolio allocation.
Sponsored by the Department of Mathematical Sciences
Host: Robert Serfling
Refreshments will be served in Room FO 2.610F 30 minutes before the talk begins.