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Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis
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Joint and Individual Component Regression.

Peiyao Wang1, Haodong Wang1, Quefeng Li2

  • 1Department of Statistics and Operations Research, University of North Carolina at Chapel Hill.

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|November 11, 2024
PubMed
Summary
This summary is machine-generated.

We introduce the Joint and Individual Component Regression (JICO) model for analyzing complex multi-group data. JICO effectively balances shared and group-specific patterns, offering flexibility and improved analysis for heterogeneous datasets.

Keywords:
Continuum RegressionHeterogeneityLatent Component RegressionMulti-group Data

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Multi-group data presents analytical challenges due to inherent heterogeneity.
  • Existing methods may struggle to capture both shared and group-specific patterns effectively.

Purpose of the Study:

  • To propose a novel Joint and Individual Component Regression (JICO) model for analyzing multi-group data.
  • To develop a flexible framework that balances global and group-specific regression components.

Main Methods:

  • The JICO model decomposes the response into shared (joint) and group-specific (individual) components.
  • Low-rank approximations of joint and individual structures are derived from predictors.
  • An iterative algorithm is proposed using Continuum Regression (CR) for latent score estimation, unifying OLS, PLS, and PCR.

Main Results:

  • JICO effectively models both commonalities across groups and unique variations within each group.
  • The model demonstrates flexibility through the use of Continuum Regression.
  • Simulation studies and an Alzheimer's disease dataset analysis confirm JICO's effectiveness.

Conclusions:

  • The JICO model offers a robust approach to analyzing heterogeneous multi-group data.
  • It provides a valuable tool for uncovering complex relationships in data with group structures.
  • An R package for JICO is publicly available for broader application.