Extremal Local Linear Quantile Regression for Heavy-Tailed Time Series with near Epoch Dependence
近历元依赖重尾时间序列的极端局部线性分位数回归
This paper develops a data-driven inference procedure for extreme analysis of data with near epoch dependence (NED), a condition less restrictive than traditional dependence structures like α-mixing, making it particularly useful for analyzing heavy-tailed time series data. To capture nonlinear data structures, we propose a new framework that combines local linear quantile regression with extreme value theory. We first study the asymptotic properties of the local-linear…