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Robust functional principal component analysis for non-Gaussian longitudinal data

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  • Zhong, Rou
  • Liu, Shishi
  • Li, Haocheng
  • Zhang, Jingxiao

Abstract

Functional principal component analysis is essential in functional data analysis, but the inference will become unconvincing when non-Gaussian characteristics occur (e.g., heavy tail and skewness). The focus of this manuscript is to develop a robust functional principal component analysis methodology to deal with non-Gaussian longitudinal data, where sparsity and irregularity along with non-negligible measurement errors must be considered. We introduce a Kendall’s τ function to handle the non-Gaussian issues. Moreover, the estimation algorithm is studied and the asymptotic theory is discussed. Our method is validated by a simulation study and it is applied to analyze a real world dataset.

Suggested Citation

  • Zhong, Rou & Liu, Shishi & Li, Haocheng & Zhang, Jingxiao, 2022. "Robust functional principal component analysis for non-Gaussian longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:jmvana:v:189:y:2022:i:c:s0047259x21001421
    DOI: 10.1016/j.jmva.2021.104864
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