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Iterated multisource exchangeability models for individualized inference with an application to mobile sensor data.

Roland Brown1, Yingling Fan2, Kirti Das2

  • 1Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota.

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|May 16, 2020
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Summary
This summary is machine-generated.

New iterated multisource exchangeability models (iMEMs) improve individualized inference by efficiently combining sensor data. This Bayesian approach enhances precision in analyzing complex, high-dimensional datasets from sources like smartphone apps.

Keywords:
heterogeneous data sourcesindividual-level inferencesensor technologysupplementary data

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Area of Science:

  • Biostatistics
  • Computational Biology
  • Data Science

Background:

  • Sensor technology is crucial for collecting activity data for individualized inference.
  • Combining population and individual data for optimal inference remains a challenge.
  • Multisource exchangeability models (MEMs) offer a Bayesian approach but struggle with high-dimensional data.

Purpose of the Study:

  • To develop an efficient method for individualized inference using multiple data sources.
  • To address the limitations of traditional MEMs in high-dimensional applications.
  • To introduce iterated MEMs (iMEMs) for scalable and precise data integration.

Main Methods:

  • Proposed iterated multisource exchangeability models (iMEMs).
  • iMEMs identify the most exchangeable data sources before fitting a MEM.
  • The method's complexity scales linearly with the number of data sources.

Main Results:

  • iMEMs significantly increase precision in individualized inference.
  • The approach maintains desirable asymptotic and small sample properties.
  • Applied to smartphone data, iMEMs achieved up to 99% efficiency gain compared to standard analyses.

Conclusions:

  • iMEMs provide a scalable and effective Bayesian framework for individualized inference.
  • The method successfully integrates heterogeneous data sources, enhancing analytical power.
  • iMEMs represent a significant advancement for leveraging sensor data in personalized research.