Robust test for independence in high dimensions |
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Authors: | Guangyu Mao |
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Affiliation: | School of Economics and Management, Beijing Jiaotong University, Beijing, China |
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Abstract: | This article develops a new test based on Spearman’s rank correlation coefficients for total independence in high dimensions. The test is robust to the non normality and heavy tails of the data, which is a merit that is not shared by the existing tests in literature. Simulation results suggest that the new test performs well under several typical null and alternative hypotheses. Besides, we employ a real data set to illustrate the use of the new test. |
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Keywords: | High-dimensional data higher-order moments independence test rank correlation |
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