Advances in Data Analysis and Classification

, Volume 13, Issue 4, pp 1083–1103 | Cite as

A Kendall correlation coefficient between functional data

  • Dalia ValenciaEmail author
  • Rosa E. Lillo
  • Juan Romo
Regular Article


Measuring dependence is a very important tool to analyze pairs of functional data. The coefficients currently available to quantify association between two sets of curves show a non robust behavior under the presence of outliers. We propose a new robust numerical measure of association for bivariate functional data. We extend in this paper Kendall coefficient for finite dimensional observations to the functional setting. We also study its statistical properties. An extensive simulation study shows the good behavior of this new measure for different types of functional data. Moreover, we apply it to establish association for real data, including microarrays time series in genetics.


Concordance Dependence Functional data Kendall’s tau 

Mathematics Subject Classification

62-07 62G35 62G09 



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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of StatisticsUniversidad Carlos III de MadridMadridSpain
  2. 2.Department of Statistics, UC3M-Santander Big Data InstituteUniversidad Carlos III de MadridMadridSpain

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