Robust functional principal components: A projection-pursuit approach

Juan Lucas Bali, Graciela Boente, David E. Tyler, Jane Ling Wang

Research output: Contribution to journalArticlepeer-review

65 Scopus citations


In many situations, data are recorded over a period of time and may be regarded as realizations of a stochastic process. In this paper, robust estimators for the principal components are considered by adapting the projection pursuit approach to the functional data setting. Our approach combines robust projection-pursuit with different smoothing methods. Consistency of the estimators are shown under mild assumptions. The performance of the classical and robust procedures are compared in a simulation study under different contamination schemes.

Original languageEnglish (US)
Pages (from-to)2852-2882
Number of pages31
JournalAnnals of Statistics
Issue number6
StatePublished - Dec 2011

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


  • Fisher-consistency
  • Functional data
  • Method of sieves
  • Outliers
  • Penalization
  • Principal component analysis
  • Robust estimation


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