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Statistical Methods & Applications

, Volume 24, Issue 2, pp 263–267 | Cite as

Discussion of “Multivariate functional outlier detection”

  • Ana Arribas-GilEmail author
  • Juan Romo
Discussion

Introduction

This interesting paper adds on the authors long and outstanding trajectory in robust statistics and , more specifically, on robust functional data analysis. We congratulate Mia Hubert, Peter Rousseeuw and Pieter Segaert for this important contribution.

As the authors point out, most of the literature to date on functional outlier detection deals with univariate functional data (one curve observed by individual). This work considers the case of p-variate functional data (p curves observed by individual). The paper discusses carefully the problem of outlier detection in this setting and starts by establishing a classification of different outlying behaviours. Then, several p-variate functional depths and distance functions are defined by integrating over time the existing or newly defined p-variate counterparts. Finally, by combining these measures, several graphical diagnostic tools are proposed.

We would like to contribute to the discussion by focusing on two aspects....

Keywords

Outlier Detection Functional Data Analysis Detection Rule Shape Outlier Functional Depth 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We would like to acknowledge Mia Hubert, Peter Rousseeuw and Pieter Segaert for kindly providing all the data sets and the R code of all the procedures described in their article.

References

  1. Arribas-Gil A, Romo J (2014) Shape outlier detection and visualisation for functional data: the outliergram. Biostatistics 15:603–619CrossRefGoogle Scholar
  2. Hubert M, Rousseeuw PJ, Segaert P (2015) Multivariate functional outlier detection. Stat Methods Appl. doi: 10.1007/s10260-015-0297-8
  3. Hyndman RJ, Shang HL (2010) Rainbow plots, bagplots, and boxplots for functional data. J Comput Graph Stat 19:29–49MathSciNetCrossRefGoogle Scholar
  4. López-Pintado S, Romo J (2011) A half-region depth for functional data. Computat Stat Data Anal 55:1679–1695CrossRefGoogle Scholar
  5. Sun Y, Genton MG (2011) Functional boxplots. J Comput Graph Stat 20:316–334MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Departamento de EstadísticaUniversidad Carlos III de MadridGetafeSpain

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