Subgroup analysis for high-dimensional functional regression

被引:4
|
作者
Zhang, Xiaochen [1 ]
Zhang, Qingzhao [2 ,3 ]
Ma, Shuangge [4 ]
Fang, Kuangnan [2 ]
机构
[1] Shandong Univ, Zhongtai Secur Inst Financial Studies, Jinan, Peoples R China
[2] Xiamen Univ, Sch Econ, Dept Stat & Data Sci, Xiamen, Peoples R China
[3] Xiamen Univ, Wang Yanan Inst Studies Econ, Xiamen, Peoples R China
[4] Yale Univ, Dept Biostat, New Haven, CT USA
基金
美国国家科学基金会; 中国国家自然科学基金; 中国博士后科学基金;
关键词
Functional data analysis; High -dimensional functional predictors; Subgroup analysis; LINEAR-REGRESSION; SELECTION; MODEL; CONVERGENCE; RATES;
D O I
10.1016/j.jmva.2022.105100
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Subgroup analysis for scalar data has been well studied in the literature. However, less has been done on functional data, especially on high-dimensional functional re-gression. In this study, we develop a high-dimensional functional regression model for simultaneous estimation and subgroup identification for a heterogeneous population. Under mild conditions, we establish the estimation and selection consistency of the proposed estimators. The proposed analysis allows the number of functional predictors and number of subgroups to increase as the sample size increases. Simulation studies demonstrate satisfactory performance of the proposed method, and it is also illustrated through a real application.(c) 2022 Elsevier Inc. All rights reserved.
引用
收藏
页数:20
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