Fast covariance estimation for sparse functional data

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Smoothing of noisy sample covariances is an important component in functional data analysis. We propose a novel covariance smoothing method based on penalized splines and associated software. The proposed method is a bivariate spline smoother that is designed for covariance smoothing and can be used for sparse functional or longitudinal data. We propose a fast algorithm for covariance smoothing using leave-one-subject-out cross-validation. Our simulations show that the proposed method compares favorably against several commonly used methods. The method is applied to a study of child growth led by one of coauthors and to a public dataset of longitudinal CD4 counts.

Original languageEnglish (US)
Pages (from-to)511-522
Number of pages12
JournalStatistics and Computing
Issue number3
StatePublished - May 1 2018


  • Bivariate smoothing
  • FACEs
  • fPCA

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics


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