统计与决策2023，Vol.39Issue(24)：29-34,6.DOI:10.13546/j.cnki.tjyjc.2023.24.005
函数型分位数回归的局部稀疏估计方法
A Local Sparse Estimation Method of Functional Quantile Regression
摘要
Abstract
This paper proposes a local sparse estimation method for functional quantile regression models that include func-tional covariates and scalar response variables.This method can be used to effectively identify vacant subintervals of coefficient functions.Firstly,the full likelihood function of function quantile regression is constructed by using asymmetric Laplacian distri-bution,and the estimator of coefficient vector is derived by EM algorithm.Secondly,a local sparse estimation method is proposed based on spline smoothing and smooth shear absolute deviation method.Numerical simulation results show that the estimation method is superior to traditional methods in different sample sizes and quantiles.Finally,an example is given to verify the effec-tiveness of the estimation method.关键词
函数型分位数回归/EM算法/SCAD惩罚/B样条Key words
functional quantile regression/EM algorithm/SCAD punishment/B-spline分类
数理科学引用本文复制引用
李纯净,张淼,赵昱榕,袁晓惠..函数型分位数回归的局部稀疏估计方法[J].统计与决策,2023,39(24):29-34,6.基金项目
吉林省教育厅科学研究一般项目(JJKH20230749KJ) （JJKH20230749KJ）
