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- W4313531475 abstract "Testing the dependency between two random variables is an important inference problem in statistics since many statistical procedures rely on the assumption that the two samples are independent. To test whether two samples are independent, a so-called HSIC (Hilbert--Schmidt Independence Criterion)-based test has been proposed. Its null distribution is approximated either by permutation or a Gamma approximation. In this paper, a new HSIC-based test is proposed. Its asymptotic null and alternative distributions are established. It is shown that the proposed test is root-n consistent. A three-cumulant matched chi-squared approximation is adopted to approximate the null distribution of the test statistic. By choosing a proper reproducing kernel, the proposed test can be applied to many different types of data including multivariate, high-dimensional, and functional data. Three simulation studies and two real data applications show that in terms of level accuracy, power, and computational cost, the proposed test outperforms several existing tests for multivariate, high-dimensional, and functional data." @default.
- W4313531475 created "2023-01-06" @default.
- W4313531475 creator A5009158041 @default.
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- W4313531475 date "2023-01-03" @default.
- W4313531475 modified "2023-09-29" @default.
- W4313531475 title "A fast and accurate kernel-based independence test with applications to high-dimensional and functional data" @default.
- W4313531475 doi "https://doi.org/10.48550/arxiv.2301.00967" @default.
- W4313531475 hasPublicationYear "2023" @default.
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