Title: Multivariate functional outlier detection
Authors: Hubert, Mia ×
Rousseeuw, Peter
Segaert, Pieter #
Issue Date: 2015
Publisher: Springer-Verlag
Series Title: Statistical Methods and Applications vol:24 pages:177-202
Abstract: Functional data are occurring more and more often in practice, and various statistical techniques have been developed to analyze them.
In this paper we consider multivariate functional data, where for each curve and each time point a p-dimensional vector of measurements is observed. For functional data the study of outlier detection has started only recently, and was mostly limited to univariate curves (p=1).
In this paper we set up a taxonomy of functional outliers,
and construct new numerical and graphical techniques for the
detection of outliers in multivariate functional data, with
univariate curves included as a special case.
Our tools include statistical depth functions and distance
measures derived from them. The methods we study are affine invariant in p-dimensional space, and do not assume elliptical or any other symmetry.
ISSN: 1618-2510
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Statistics Section
× corresponding author
# (joint) last author

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