An Online Updating Approach for Estimating and Testing Mediation Effects with Big Data Streams

An Online Updating Approach for Estimating and Testing Mediation Effects with Big Data Streams

연구 분야: Databases

학회: Statistics and Computing

The use of mediation analysis has become increasingly popular across 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. This study also investigates the adjusted Sobel-type confidence interval for mediation effect within the framework of streaming data. We conduct a substantial number of simulations to evaluate the performance of the proposed method. Two real-world examples are employed to showcase the practical applicability of this approach.