Bag of little bootstraps for massive and distributed longitudinal data

Xinkai Zhou, Jin J. Zhou, Hua Zhou

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
JournalStatistical Analysis and Data Mining
DOIs
StateAccepted/In press - 2021
Externally publishedYes

Keywords

  • bags of little bootstraps
  • big data
  • EMR
  • linear mixed models
  • longitudinal data
  • parallel and distributed computing

ASJC Scopus subject areas

  • Analysis
  • Information Systems
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'Bag of little bootstraps for massive and distributed longitudinal data'. Together they form a unique fingerprint.

Cite this