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A nonparametric test of independence between 2 variables
Author(s) -
Li Bin,
Yu Qingzhao
Publication year - 2017
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11363
Subject(s) - nonparametric statistics , statistic , independence (probability theory) , computer science , test statistic , algorithm , data mining , statistics , statistical hypothesis testing , noise (video) , pattern recognition (psychology) , mathematics , artificial intelligence , image (mathematics)
A nonparametric statistic, called the roughness of concomitant ranks, is proposed for testing whether 2 quantitative vectors are dependent. The new testing procedure is highly computationally efficient and simple, and exhibits competitive empirical performance in simulations and 2 microarray data analyses. We apply the new method to screen variables for high‐dimensional data analysis. For a low signal‐to‐noise ratio setting, we suggest the use of data binning to increase the power of the test. Simulation results show the fine performance of the proposed method with the existing screening methods.