Bagplots, boxplots, and outlier detection for functional data

Rob J. Hyndman*, Han Lin Shang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

We propose some new tools for visualizing functional data and for identifying functional outliers. The proposed tools make use of robust principal component analysis, data depth and highest density regions. We compare the proposed outlier detection methods with the existing “functional depth” method, and show that our methods have better performance on identifying outliers in French male age-specific mortality data.
Original languageEnglish
Title of host publicationFunctional and operatorial statistics
EditorsSophie Dabo-Niang, Frâedâeric Ferraty
Place of PublicationHeidelberg
PublisherSpringer
Pages201-207
Number of pages7
ISBN (Electronic)9783790820621
ISBN (Print)9783790820614
DOIs
Publication statusPublished - 2008
Externally publishedYes
EventInternational Workshop on Functional and Operatorial Statistics (1st : 2008) - Toulouse, France
Duration: 19 Jun 200821 Jun 2008

Publication series

NameContributions to statistics
PublisherSpringer
ISSN (Print)1431-1941

Conference

ConferenceInternational Workshop on Functional and Operatorial Statistics (1st : 2008)
CountryFrance
CityToulouse
Period19/06/0821/06/08

Keywords

  • Functional Data
  • Outlier Detection
  • Principal Component Score
  • High Density Region
  • Robust Principal Component Analysis

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