Functional generalized canonical correlation analysis for studying multiple longitudinal variables.

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  • Additional Information
    • Source:
      Publisher: Biometric Society Country of Publication: England NLM ID: 0370625 Publication Model: Print Cited Medium: Internet ISSN: 1541-0420 (Electronic) Linking ISSN: 0006341X NLM ISO Abbreviation: Biometrics Subsets: MEDLINE
    • Publication Information:
      Publication: Alexandria Va : Biometric Society
      Original Publication: Washington.
    • Subject Terms:
      Bayes Theorem* ; Computer Simulation* ; Models, Statistical*; Longitudinal Studies ; Humans ; Biometry/methods ; Data Interpretation, Statistical
    • Abstract:
      In this paper, we introduce functional generalized canonical correlation analysis, a new framework for exploring associations between multiple random processes observed jointly. The framework is based on the multiblock regularized generalized canonical correlation analysis framework. It is robust to sparsely and irregularly observed data, making it applicable in many settings. We establish the monotonic property of the solving procedure and introduce a Bayesian approach for estimating canonical components. We propose an extension of the framework that allows the integration of a univariate or multivariate response into the analysis, paving the way for predictive applications. We evaluate the method's efficiency in simulation studies and present a use case on a longitudinal dataset.
      (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)
    • Contributed Indexing:
      Keywords: functional data; generalized canonical correlation analysis; longitudinal data
    • Publication Date:
      Date Created: 20241021 Date Completed: 20241021 Latest Revision: 20241021
    • Publication Date:
      20241022
    • Accession Number:
      10.1093/biomtc/ujae113
    • Accession Number:
      39432444