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A Reverse Genetic Approach to Test Functional Redundancy During Embryogenesis
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Functional Extended Redundancy Analysis.

Heungsun Hwang1, Hye Won Suk2, Jang-Han Lee3

  • 1Department of Psychology, McGill University, 1205 Dr. Penfield Avenue, Montreal, QC, H3A 1B1, Canada. heungsun.hwang@mcgill.ca.

Psychometrika
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PubMed
Summary
This summary is machine-generated.

This study introduces functional extended redundancy analysis to explore directional relationships among multivariate variables over continua like time or space. The method uses penalized least squares and a novel algorithm for empirical feasibility.

Keywords:
alternating regularized least-squares algorithmextended redundancy analysisfunctional datapenalized least squares

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Area of Science:

  • Multivariate statistics
  • Functional data analysis

Background:

  • Extended redundancy analysis (eXDA) models relationships between variable sets.
  • Existing methods may not fully capture dynamic or continuous variable changes.

Purpose of the Study:

  • To develop a functional version of extended redundancy analysis (fXDA).
  • To investigate directional relationships among functional multivariate variables.
  • To extend eXDA for variables varying over time, space, or other continua.

Main Methods:

  • Minimizing a penalized least-squares criterion.
  • Employing basis function expansion for function approximation.
  • Utilizing an alternating regularized least-squares algorithm.

Main Results:

  • The proposed functional extended redundancy analysis method is computationally feasible.
  • Demonstrated empirical application on real-world datasets.

Conclusions:

  • The developed functional extended redundancy analysis provides a new tool for analyzing complex multivariate relationships.
  • This method effectively models directional influences among functional variables.