Inference for High-Dimensional Streamed Longitudinal Data  

作　　者：Senyuan Zheng Ling Zhou 

机构地区：[1]Center of Statistical Research and School of Statistics,Southwestern University of Finance and Economics,Chengdu 611130,P.R.China

出　　处：《Acta Mathematica Sinica,English Series》2025年第2期757-779,共23页数学学报(英文版)

基　　金：Supported by National Key R&D Program of China(Grant No.2022YFA1003702);National Natural Science Foundation of China(Grant No.12271441)。

摘　　要：With the advent of modern devices,such as smartphones and wearable devices,high-dimensional data are collected on many participants for a period of time or even in perpetuity.For this type of data,dependencies between and within data batches exist because data are collected from the same individual over time.Under the framework of streamed data,individual historical data are not available due to the storage and computation burden.It is urgent to develop computationally efficient methods with statistical guarantees to analyze high-dimensional streamed data and make reliable inferences in practice.In addition,the homogeneity assumption on the model parameters may not be valid in practice over time.To address the above issues,in this paper,we develop a new renewable debiased-lasso inference method for high-dimensional streamed data allowing dependences between and within data batches to exist and model parameters to gradually change.We establish the large sample properties of the proposed estimators,including consistency and asymptotic normality.The numerical results,including simulations and real data analysis,show the superior performance of the proposed method.

关 键 词：Debiased lasso high-dimensional inference streamed longitudinal data renewable inference 

分 类 号：O212.1[理学—概率论与数理统计]