Preprocessing noisy functional data: a multivariate perspective

Siegfried Hörmann, Fatima Jammoul*

*Corresponding author for this work

Research output: Contribution to journalArticle


We consider functional data which are measured on a discrete set of observation points. Often such data are measured with additional noise. We explore in this paper the factor structure underlying this type of data. We show that the latent signal can be attributed to the common components of a corresponding factor model and can be estimated accord-ingly, by borrowing methods from factor model literature. We also show that principal components, which play a key role in functional data anal-ysis, can be accurately estimated by taking such a multivariate instead of a ‘functional’ perspective. In addition to the estimation problem, we also address testing of the null-hypothesis of iid noise. While this assumption is largely prevailing in the literature, we believe that it is often unrealistic and not supported by a residual analysis.

Original languageEnglish
Pages (from-to)6232-6266
Number of pages35
JournalElectronic Journal of Statistics
Issue number2
Publication statusPublished - 2022


  • stat.ME
  • stat.ML
  • Functional data
  • preprocessing
  • signal-plus-noise
  • factor models
  • high-dimensional statistics

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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