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  • 1Department of Statistics and Actuarial Science, University of Iowa, Iowa City, Iowa, USA.

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Summary

This study introduces an efficient online method for analyzing distributed streaming data from multiple sites using linear mixed-effects models. The approach effectively handles data heterogeneity and correlation, providing accurate estimates without needing past data.

Keywords:
collaborative inferencedivide-and-conquerlinear mixed-effects modelsmeta-analysisonline learning

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Area of Science:

  • Statistics
  • Data Science
  • Machine Learning

Background:

  • Analyzing distributed streaming data presents challenges in managing heterogeneity and correlation.
  • Existing methods often require re-accessing historical data, which is inefficient for continuous streams.

Purpose of the Study:

  • To develop an online estimation and inference method for distributed streaming data.
  • To address both between-site heterogeneity and within-site correlation in continuous data streams.

Main Methods:

  • Proposed an online two-way approach using linear mixed-effects models.
  • Modeled site-specific effects as random-effect terms.
  • Developed an efficient online updating procedure for parameter estimation.

Main Results:

  • Derived a non-asymptotic error bound for the online estimator.
  • Demonstrated asymptotic equivalence to offline methods.
  • Showcased advantages over alternative solutions analytically and numerically.

Conclusions:

  • The proposed online method provides an efficient and accurate solution for analyzing distributed streaming data.
  • The approach effectively handles complex data structures, including heterogeneity and correlation.
  • The method is validated through theoretical analysis and practical data applications.