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  3. Multilevel Functional Quantile Principal Component Analysis.
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  3. Multilevel Functional Quantile Principal Component Analysis.

Related Experiment Video

Quantification of Orofacial Phenotypes in Xenopus
09:26

Quantification of Orofacial Phenotypes in Xenopus

Published on: November 6, 2014

Multilevel functional quantile principal component analysis.

Álvaro Méndez-Civieta1,2, Ying Wei1, Jeff Goldsmith1

  • 1Department of Biostatistics, Columbia University, 722 W 178 St, New York,NY 10032, United States.

Biostatistics (Oxford, England)
|June 15, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

A new method, Multilevel Functional Quantile Principal Component Analysis (MFQPCA), analyzes physical activity data from congestive heart failure patients. It reveals day-to-day variations in sedentary behavior and individual differences in vigorous activity.

Keywords:
accelerometer datadimension reductionhierarchical functional datamultilevel analysisquantile regression

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Accelerometer data collection has advanced to long-term monitoring (weeks to years).
  • Understanding physical activity requires analyzing variability beyond average values.
  • Hierarchical data structures are common in health and activity monitoring.

Purpose of the Study:

  • Introduce Multilevel Functional Quantile Principal Component Analysis (MFQPCA), a novel dimension-reduction technique.
  • Extend Functional Quantile Principal Analysis to hierarchical functional data.
  • Quantify and understand physical activity patterns beyond expected values in complex datasets.

Main Methods:

  • Developed MFQPCA to decompose quantile-specific variability in hierarchical functional data.
  • Applied MFQPCA to accelerometry data from congestive heart failure patients (4-9 months).
  • Estimated between-participant and within-participant variability at different quantile levels (10%-90%).

    • Main Results:

    • MFQPCA effectively captures complex distributional features and disentangles variability sources.
    • Revealed that day-to-day variability is dominant in sedentary periods.
    • Showed that between-participant differences increase with vigorous activity intensity.

    Conclusions:

    • MFQPCA provides a robust method for analyzing longitudinal changes in hierarchical functional data.
    • The method facilitates a deeper understanding of physical activity patterns and their variability.
    • An open-source R package (FunQ) makes MFQPCA accessible for broad applications.