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Limit laws of the empirical Wasserstein distance: Gaussian distributions.

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Munk,  A.
Research Group of Statistical Inverse-Problems in Biophysics, MPI for biophysical chemistry, Max Planck Society;

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Citation

Rippl, T., Munk, A., & Sturm, A. (2016). Limit laws of the empirical Wasserstein distance: Gaussian distributions. Journal of Multivariate Analysis, 151, 90-109. doi:10.1016/j.jmva.2016.06.005.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002B-9A7C-E
Abstract
We derive central limit theorems for the Wasserstein distance between the empirical distributions of Gaussian samples. The cases are distinguished whether the underlying laws are the same or different. Results are based on the (quadratic) Frechet differentiability of the Wasserstein distance in the gaussian case. Extensions to elliptically symmetric distributions are discussed as well as several applications such as bootstrap and statistical testing.