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Activity Number: 98 - Student Paper Award and John M. Chambers Statistical Software Award
Type: Topic Contributed
Date/Time: Monday, August 8, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Graphics
Abstract #322161
Title: Bag of Little Bootstraps for Massive and Distributed Longitudinal Data
Author(s): Xinkai Zhou* and Jin Zhou and Hua Zhou
Companies: UCLA and UCLA and UCLA
Keywords: bags of little bootstraps; linear mixed model; longitudinal data; big data; EMR

Linear mixed models are widely used for analyzing longitudinal datasets, and the inference for variance component parameters relies on the bootstrap method. However, health systems and technology companies routinely generate massive longitudinal datasets that make the traditional bootstrap method infeasible. To solve this problem, we extend the highly scalable bag of little bootstraps method for independent data to longitudinal data and develop a highly efficient Julia package MixedModelsBLB.jl. Simulation experiments and real data analysis demonstrate the favorable statistical performance and computational advantages of our method compared to the traditional bootstrap method. For the statistical inference of variance components, it achieves 200 times speedup on the scale of 1 million subjects (20 million total observations), and is the only currently available tool that can handle more than 10 million subjects (200 million total observations) using desktop computers.

Authors who are presenting talks have a * after their name.

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