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Correcting Measurement Error and Zero Inflation in Functional Covariates for Scalar-on-Function Quantile Regression.

Caihong Qin1, Lan Xue2, Ufuk Beyaztas3

  • 1Department of Epidemiology and Biostatistics, School of Public Health, Indiana University, Bloomington, Indiana, USA.

Statistics in Medicine
|May 15, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model for wearable device data, addressing measurement errors and excess zeros. The method improves accuracy in analyzing physical activity and health outcomes.

Keywords:
childhood obesityexcess zerosfunctional dataphysical activitywearable devices

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Area of Science:

  • Biostatistics
  • Wearable Technology
  • Public Health

Background:

  • Wearable devices generate valuable time-varying biobehavioral data for health research.
  • Existing statistical methods struggle to simultaneously address measurement error and excess zeros in this data.

Purpose of the Study:

  • To develop a novel statistical framework for analyzing zero-inflated and error-prone functional data from wearable devices.
  • To accurately estimate latent health behaviors and their impact on outcomes.

Main Methods:

  • Introduced a modeling framework with a subject-specific time-varying validity indicator.
  • Employed maximum likelihood estimation, basis expansions, and linear mixed models.
  • Utilized joint quantile regression to assess covariate effects.

Main Results:

  • The proposed approach significantly enhances estimation accuracy compared to methods addressing only measurement error.
  • Joint estimation in quantile regression shows substantial improvements over separate analyses.
  • The model effectively corrects for zero inflation and measurement error in step count data.

Conclusions:

  • The novel framework accurately models complex wearable sensor data, accounting for inherent data issues.
  • This approach provides a more reliable method for using physical activity proxies like step counts in health studies.
  • Findings support the use of corrected step count data for understanding childhood obesity and physical activity.