时 间：2024年6月21日 10:00 - 11:00
地 点：普陀校区理科大楼A1514
报告人：张霆 佐治亚大学副教授
主持人：周迎春 华东师范大学教授
摘 要：
Quantile regression is a popular and powerful method for studying the effect of regressors on quantiles of a response distribution. However, existing results on quantile regression were mainly developed for cases in which the quantile level is fixed, and the data are often assumed to be independent. Motivated by recent applications, we consider the situation where (i) the quantile level is not fixed and can grow with the sample size to capture the tail phenomena, and (ii) the data are no longer independent, but collected as a time series that can exhibit serial dependence in both tail and non-tail regions. To study the asymptotic theory for high-quantile regression estimators in the time series setting, we introduce a tail adversarial stability condition, which had not previously been described, and show that it leads to an interpretable and convenient framework for obtaining limit theorems for time series that exhibit serial dependence in the tail region, but are not necessarily strongly mixing. Numerical experiments are conducted to illustrate the effect of tail dependence on high-quantile regression estimators, for which simply ignoring the tail dependence may yield misleading p-values.
报告人简介：
Ting Zhang is currently Associate Professor in the Department of Statistics at the University of Georgia. He obtained his Ph.D. in Statistics from The University of Chicago, and his research interests include tail dependent time series, high-dimensional data, nonparametric and semiparametric inference, nonstationary nonlinear processes, and self-normalization. His research has been published in Biometrika, Journal of the American Statistical Association, The Annals of Statistics, Journal of Econometrics, IEEE Transactions on Signal Processing, Econometric Theory, and many of them are with his students. He is also a recipient of the prestigious U.S. National Science Foundation’s CAREER Award.