A Kendall correlation coefficient between functional data

Dalia Valencia (), Rosa E. Lillo () and Juan Romo ()
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Dalia Valencia: Universidad Carlos III de Madrid
Rosa E. Lillo: Universidad Carlos III de Madrid
Juan Romo: Universidad Carlos III de Madrid

Advances in Data Analysis and Classification, 2019, vol. 13, issue 4, No 11, 1083-1103

Abstract: Abstract 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.

Keywords: Concordance; Dependence; Functional data; Kendall’s tau; 62-07; 62G35; 62G09 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11634-019-00360-z

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