大数据流中介效应估计与检验的在线更新方法
An Online Updating Approach for Estimating and Testing Mediation Effects with Big Data Streams
AI总结：
针对大数据流中介效应分析的计算负担问题，提出在线更新方法，高效估计与检验线性和逻辑中介模型中的中介效应，提升多种检验效率，并经模拟和实例验证。
摘要：
近年来，中介分析在各研究领域中的应用日益普及。中介分析的主要目标是检验从暴露到结局的路径上的间接效应。与此同时，数据采集技术的出现引发了人们对新兴的大数据分析领域的浓厚兴趣，其中流式数据集的中介分析近来受到广泛关注。然而，数据的庞大规模导致了计算负担的增加。本研究提出了一种在线更新方法来解决这一问题，旨在在大规模数据流的线性和逻辑中介模型背景下估计和检验中介效应。所提出的算法显著提高了Sobel检验、调整后Sobel检验、联合显著性检验和调整后联合显著性检验的计算效率。我们进行了大量的数值模拟来评估这种可再生方法的性能。两个真实世界的实例被用来展示该方法的实际适用性。
英文摘要：
The use of mediation analysis has become increasingly popular in various research fields in recent years. The primary objective of mediation analysis is to examine the indirect effects along the pathways from exposure to outcome. Meanwhile, the advent of data collection technology has sparked a surge of interest in the burgeoning field of big data analysis, where mediation analysis of streaming data sets has recently garnered significant attention. The enormity of the data, however, results in an augmented computational burden. The present study proposes an online updating approach to address this issue, aiming to estimate and test mediation effects in the context of linear and logistic mediation models with massive data streams. The proposed algorithm significantly enhances the computational efficiency of Sobel test, adjusted Sobel test, joint significance test, and adjusted joint significance test. We conduct a substantial number of numerical simulations to evaluate the performance of the renewable method. Two real-world examples are employed to showcase the practical applicability of this approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
