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Latent factor model for multivariate functional data

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  • Ruonan Li
  • Luo Xiao

Abstract

For multivariate functional data, a functional latent factor model is proposed, extending the traditional latent factor model for multivariate data. The proposed model uses unobserved stochastic processes to induce the dependence among the different functions, and thus, for a large number of functions, may provide a more parsimonious and interpretable characterization of the otherwise complex dependencies between the functions. Sufficient conditions are provided to establish the identifiability of the proposed model. The performance of the proposed model is assessed through simulation studies and an application to electroencephalography data.

Suggested Citation

  • Ruonan Li & Luo Xiao, 2023. "Latent factor model for multivariate functional data," Biometrics, The International Biometric Society, vol. 79(4), pages 3307-3318, December.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:4:p:3307-3318
    DOI: 10.1111/biom.13924
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    References listed on IDEAS

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