Distance-based and RKHS-based dependence metrics in high dimension
نویسندگان
چکیده
منابع مشابه
Equivalence of Distance-based and Rkhs-based Statistics in Hypothesis Testing by Dino Sejdinovic, Bharath Sriperumbudur,
We provide a unifying framework linking two classes of statistics used in two-sample and independence testing: on the one hand, the energy distances and distance covariances from the statistics literature; on the other, maximum mean discrepancies (MMD), that is, distances between embeddings of distributions to reproducing kernel Hilbert spaces (RKHS), as established in machine learning. In the ...
متن کاملEquivalence of distance-based and RKHS-based statistics in hypothesis testing
We provide a unifying framework linking two classes of statistics used in two-sample and independence testing: on the one hand, the energy distances and distance covariances from the statistics literature; on the other, Maximum Mean Discrepancies (MMD), i.e., distances between embeddings of distributions to reproducing kernel Hilbert spaces (RKHS), as established in machine learning. In the cas...
متن کاملEquivalence of Distance-based and Rkhs-based Statistics in Hypothesis Testing by Dino Sejdinovic,
We provide a unifying framework linking two classes of statistics used in two-sample and independence testing: on the one hand, the energy distances and distance covariances from the statistics literature; on the other, maximum mean discrepancies (MMD), that is, distances between embeddings of distributions to reproducing kernel Hilbert spaces (RKHS), as established in machine learning. In the ...
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Dimension reduction based on extreme dependence
We introduce a dimension reduction technique based on extreme observations. The classical assumption of a linear model for the distribution of a random vector is replaced by the weaker assumption of a fairly general model for the copula. We assume an elliptical copula to describe the extreme dependence structure, which preserves a ’correlation-like’ structure in the extremes. Based on the tail ...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2020
ISSN: 0090-5364
DOI: 10.1214/19-aos1934