Estimation in Partially Observed Functional Linear Quantile Regression

• 作者: XIAO Juxia XIE Tianfa ZHANG Zhongzhan
• 来源期刊: 系统科学与复杂性学报（英文版）
• 年，卷（期）: 2022,（1）
• 页码范围: 313-341
• 页数: 29页
• 语种: 英文
• DOI: 10.1007/s11424-020-0019-7

摘要

Currently,working with partially observed functional data has attracted a greatly increas-ing attention,since there are many applications in which each functional curve may be observed only on a subset of a common domain,and the incompleteness makes most existing methods for functional data analysis ineffective.In this paper,motivated by the appealing characteristics of conditional quan-tile regression,the authors consider the functional linear quantile regression,assuming the explanatory functions are observed partially on dense but discrete point grids of some random subintervals of the domain.A functional principal component analysis (FPCA) based estimator is proposed for the slope function,and the convergence rate of the estimator is investigated.In addition,the finite sample per-formance of the proposed estimator is evaluated through simulation studies and a real data application.