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Extremal correlation coefficient for functional data
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AI
DOI：10.1093/biomet/asaf077.png)
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Abstract
En 中文
We propose a coefficient that measures extremal dependence in paired samples of functions. It has properties similar to the Pearson correlation, but differs in two significant ways: (i) it is designed to measure dependence between curves; and (ii) it focuses only on extreme curves. The new coefficient is derived within the framework of regular variation in Banach spaces. A consistent estimator is proposed and justified by an asymptotic analysis and a simulation study. The usefulness of the new coefficient is illustrated using financial and climate functional data.
Keywords:
Correlation
Extremes
Functional data
Journal
B
IF:
2.8
Papers:
34
Citations:
0