A0539
Title: Robust estimation of high dimensional time series
Authors: Danna Zhang - University of California, San Diego (United States) [presenting]
Abstract: High dimensional non-Gaussian time series data are increasingly encountered in various applications. It makes many traditional statistical analysis tools for independent data infeasible and poses a great challenge in developing new tools for time series. A novel Bernstein-type inequality for high-dimensional time series shall be presented. Then it is applied to investigate two high-dimensional robust estimation problems: (1) time series regression with fat-tailed and correlated covariates and errors, (2) fat-tailed vector autoregression. As a natural requirement of consistency, the dimension can be allowed to increase exponentially with the sample size under a very mild moment and dependence conditions.
