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Showing 1 to 3 of 3 for “"Distance covariance"”.

  1. Dependence testing in high dimension

    … The test is constructed based on the pairwise distance covariance and it accounts for the non-linear and non-monotone dependencies among the data. Our test can be conveniently implemented in practice as the limiting null distribution of the test statistic is shown to be standard normal. It …

    uiuc Repository record for Dependence testing in high dimension (opens in a new tab)

  2. Statistical inference for high-dimensional data

    … proposed methods. In the first chapter, we study distance covariance, Hilbert-Schmidt covariance (aka Hilbert-Schmidt independence criterion [Gretton et al. (2008)] and related independence tests under the high dimensional scenario. We show that the sample distance/Hilbert-Schmidt covariance

    uiuc Repository record for Statistical inference for high-dimensional data (opens in a new tab)

  3. Network Inference Using Independence Criteria

    … the most popular general independence criteria: distance covariance (dCov), kernel canonical variance (KCC), kernel generalized variance (KGV) and the Hilbert-Schmidt Independence Criterion (HSIC). We provide easy to understand geometrical interpretations for these criteria. We also explicitly …

    cambridge Repository record for Network Inference Using Independence Criteria (opens in a new tab)