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.

中文翻译：

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功能数据之间的Kendall相关系数

测量依赖性是分析功能数据对的非常重要的工具。当前可用于量化两组曲线之间的关联的系数在存在异常值的情况下显示出非稳健的行为。我们为双变量功能数据提出了一种新的强大的关联数值度量。在本文中，我们将用于有限维观测的肯德尔系数扩展到功能设置。我们还研究了其统计特性。广泛的仿真研究表明，此新措施对于不同类型的功能数据具有良好的性能。此外，我们将其用于建立真实数据的关联，包括遗传学中的微阵列时间序列。