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Bag of little bootstraps for massive and distributed longitudinal data
Author(s) -
Zhou Xinkai,
Zhou Jin J.,
Zhou Hua
Publication year - 2022
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11563
Subject(s) - computer science , inference , scalability , statistical inference , variance (accounting) , data mining , speedup , computational statistics , bootstrap model , variance components , statistics , machine learning , artificial intelligence , parallel computing , database , mathematics , boson , physics , accounting , particle physics , particle decay , business
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.