Composite Quantile Regression for Functional Linear Models with Dependent Errors
WENG Yuling; YU Ping; ZHANG Zhongzhan
College of Applied Sciences, Beijing University of Technology, Beijing, 100124, China; Department of Statistics, Fudan University, Shanghai, 200433, China; College of Mathematics and Computer Science,Shanxi Normal University, Linfen, 041000, China
n this paper, we propose composite quantile regression for functional linear model with dependent data, in which the errors are from a short-range dependent and strictly stationary linear process. The functional principal component analysis is employed to approximate the slope function and the functional predictive variable respectively to construct an estimator of the slope function, and the convergence rate of the estimator is obtained under some regularity conditions. Simulation studies and a real data analysis are presented for illustration of the performance of the proposed estimator.