Subgroup analysis for high-dimensional functional regression
Subgroup analysis for high-dimensional functional regression
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DOI：
10.1016/j.jmva.2022.105100
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Xiaochen Zhang;Qingzhao Zhang;Shuangge Ma;Kuangnan Fang
中科院分区：
文献类型：
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作者：
Xiaochen Zhang;Qingzhao Zhang;Shuangge Ma;Kuangnan Fang
标量数据的亚组分析在文献中已得到充分研究。然而，对函数数据的研究较少，尤其是在高维函数回归方面。在本研究中，我们开发了一个高维函数回归模型，用于异质群体的同步估计和亚组识别。在温和的条件下，我们建立了所提出的估计量的估计和选择一致性。所提出的分析允许功能预测变量的数量和子组的数量随着样本量的增加而增加。仿真研究证明了所提出方法的令人满意的性能，并且还通过实际应用进行了说明。
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 regression. 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.