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Fixed-effects inference and tests of correlation for longitudinal functional data.

Ruonan Li1, Luo Xiao1, Ekaterina Smirnova2

  • 1Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA.

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|May 1, 2022
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Summary
This summary is machine-generated.

This study introduces a new statistical framework for analyzing longitudinal functional data, enhancing correlation models for scalar and functional observations. Methods were validated through simulations and applied to physical activity data.

Keywords:
accelerometry datacovariance functionhypothesis testmixed effects model

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Standard longitudinal models handle scalar data, but functional data requires specialized approaches.
  • Understanding correlation structures is crucial for accurate inference in repeated measures studies.

Purpose of the Study:

  • To develop an inferential framework for fixed effects in longitudinal functional models.
  • To introduce statistical tests for correlation structures in longitudinal functional data.
  • To extend existing longitudinal correlation models to functional observations.

Main Methods:

  • Proposed an inferential framework for fixed effects in longitudinal functional models.
  • Developed tests for correlation structures arising from longitudinal sampling.
  • Conducted simulation studies to compare estimation under different correlation structure specifications.
  • Applied the methods to a physical activity longitudinal functional dataset.

Main Results:

  • The proposed framework effectively handles fixed effects in longitudinal functional models.
  • Simulation studies demonstrated the performance of estimation and correlation structure testing.
  • The methods were successfully applied to real-world physical activity data.

Conclusions:

  • The developed framework provides a robust extension for analyzing longitudinal functional data.
  • The introduced tests are valuable for assessing correlation structures in such data.
  • The approach offers practical utility for researchers in various fields studying dynamic processes.