Wasserstein and weighted metrics for multidimensional Gaussian distributions

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We present a number of low and upper bounds for Levy – Prokhorov, Wasserstein, Frechet, and Hellinger distances between probability distributions of the same or different dimensions. The weighted (or context-sensitive) total variance and Hellinger  distances are introduced. The upper and low bounds for these weighted metrics are proved. The low bounds for the minimum of different errors in sensitive hypothesis testing are proved. 

作者简介

Mark Kelbert

Higher School of Economics – National Research University

ORCID iD: 0000-0002-3952-2012
Scopus 作者 ID: 55884702600
20 Myasnitskaya St., Moscow 101000, Russia

Yurii Suhov

DPMMS, Penn State University; University of Cambridge

Scopus 作者 ID: 35582648200
201 Old Main, State College, PA 16802, USA

参考

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