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Clustering multivariate functional data with phase variation.

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  • 1Department of Mathematics and Statistics, Lancaster University, Lancaster LA1 4YF, UK.

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Summary
This summary is machine-generated.

This study introduces a novel warping framework for clustering multivariate functional data with phase variations. It effectively identifies distinct relative growth patterns in body parts for different individuals.

Keywords:
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Area of Science:

  • Statistics
  • Functional Data Analysis
  • Biometrics

Background:

  • Characterizing variations in multivariate functional data with multiple curves per subject is challenging.
  • Phase and amplitude variations complicate the analysis of such data.

Purpose of the Study:

  • To develop a method for clustering multivariate functional data exhibiting phase variations.
  • To extract relevant features for clustering by addressing variations in both amplitude and phase.

Main Methods:

  • A conditional subject-specific warping framework is proposed.
  • The method is applied to multivariate growth curves of body parts.

Main Results:

  • The proposed approach effectively extracts features for clustering.
  • Identified clusters reveal distinct relative growth patterns among body parts within individuals.

Conclusions:

  • The developed framework successfully clusters multivariate functional data with phase variations.
  • It provides insights into heterogeneous growth patterns across different body segments.