Renewable penalized linear regression via inverse probability weighting for streaming data with missing covariates

IF 1.6 3区数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computational Statistics & Data Analysis Pub Date : 2026-07-01 Epub Date: 2026-01-24 DOI:10.1016/j.csda.2025.108338

Kang Meng, Yujie Gai

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引用次数: 0

Abstract

A renewable weighted estimation method for linear regression with non-convex regularization is proposed, tailored for streaming data with missing covariates. The proposed method is implemented via a two-step estimation strategy. In the first step, a renewable formulation of the parameter of interest in the propensity score function is derived. Based on this, a renewable weighted optimization objective for the regression coefficients is constructed in the second step, which is updated using the current data and summary statistics from historical data. The objective is solved via a locally adaptive majorize-minimization algorithm with previous estimates as initialization, while the penalty parameter is determined using the proposed online rolling validation procedure. Theoretical results demonstrate that the renewable estimator is asymptotically normal and maintains estimation efficiency compared to offline methods that process all data at once. Simulation studies and real data analysis further confirm that the proposed estimator achieves competitive statistical performance while significantly improving computational efficiency and reducing memory requirements.

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可再生通过对缺少协变量的流数据的逆概率加权惩罚线性回归

针对协变量缺失的流数据，提出了一种非凸正则化线性回归的可更新加权估计方法。该方法通过两步估计策略实现。在第一步中，导出了倾向得分函数中感兴趣参数的可更新公式。在此基础上，第二步构建回归系数的可更新加权优化目标，利用当前数据和历史数据的汇总统计更新目标。该算法采用局部自适应最大-最小算法求解目标，初始化算法以先前的估计值为初始化，同时采用所提出的在线滚动验证程序确定惩罚参数。理论结果表明，与一次性处理所有数据的离线方法相比，可再生估计器是渐近正态的，并且保持了估计效率。仿真研究和实际数据分析进一步证实，该估计器在显著提高计算效率和降低内存需求的同时，实现了具有竞争力的统计性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational Statistics & Data Analysis 数学-计算机：跨学科应用

CiteScore

3.70

自引率

5.60%

发文量

167

审稿时长

60 days

期刊介绍： Computational Statistics and Data Analysis (CSDA), an Official Publication of the network Computational and Methodological Statistics (CMStatistics) and of the International Association for Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of four refereed sections which are divided into the following subject areas: I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics,computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, heuristics, knowledge based systems, machine learning, neural networks, numerical and optimization methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms. Software and algorithms can be submitted with manuscripts and will be stored together with the online article. II) Statistical Methodology for Data Analysis - Manuscripts dealing with novel and original data analytical strategies and methodologies applied in biostatistics (design and analytic methods for clinical trials, epidemiological studies, statistical genetics, or genetic/environmental interactions), chemometrics, classification, data exploration, density estimation, design of experiments, environmetrics, education, image analysis, marketing, model free data exploration, pattern recognition, psychometrics, statistical physics, image processing, robust procedures. [...] III) Special Applications - [...] IV) Annals of Statistical Data Science [...]