fastWKendall: an efficient algorithm for weighted Kendall correlation

Jie Lin (), Donald A. Adjeroh (), Bing-Hua Jiang () and Yue Jiang ()
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Jie Lin: Fujian Normal University
Donald A. Adjeroh: West Virginia University
Bing-Hua Jiang: Carver College of Medicine, the University of Iowa
Yue Jiang: Fujian Normal University

Computational Statistics, 2018, vol. 33, issue 4, No 11, 1823-1845

Abstract: Abstract The Kendall correlation is a non-parametric method that measures the strength of dependence between two sequences. Like Pearson correlation and Spearman correlation, Kendall correlation is widely applied in sequence similarity measurements and cluster analysis. We propose an efficient algorithm, fastWKendall, to compute the approximate weighted Kendall correlation in $$O(n\log n)$$ O ( n log n ) time and O(n) space complexity. This is an improvement to the state-of-the-art $$O(n^2)$$ O ( n 2 ) time requirement. The proposed method can be incorporated to perform conventional sequential similarity measurement and cluster analysis much more rapidly. This is important for analysis of huge-volume datasets, such as genome databases, streaming stock market data, and publicly available huge datasets on the Internet. The code which is implemented in R is available for public access.

Keywords: Nonparametric statistics; Sequence analysis; Cluster analysis; Similarity analysis; Sequence similarity measurement (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s00180-017-0775-6

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