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May 2019 Fréchet means and Procrustes analysis in Wasserstein space
Yoav Zemel, Victor M. Panaretos
Bernoulli 25(2): 932-976 (May 2019). DOI: 10.3150/17-BEJ1009

Abstract

We consider two statistical problems at the intersection of functional and non-Euclidean data analysis: the determination of a Fréchet mean in the Wasserstein space of multivariate distributions; and the optimal registration of deformed random measures and point processes. We elucidate how the two problems are linked, each being in a sense dual to the other. We first study the finite sample version of the problem in the continuum. Exploiting the tangent bundle structure of Wasserstein space, we deduce the Fréchet mean via gradient descent. We show that this is equivalent to a Procrustes analysis for the registration maps, thus only requiring successive solutions to pairwise optimal coupling problems. We then study the population version of the problem, focussing on inference and stability: in practice, the data are i.i.d. realisations from a law on Wasserstein space, and indeed their observation is discrete, where one observes a proxy finite sample or point process. We construct regularised nonparametric estimators, and prove their consistency for the population mean, and uniform consistency for the population Procrustes registration maps.

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Yoav Zemel. Victor M. Panaretos. "Fréchet means and Procrustes analysis in Wasserstein space." Bernoulli 25 (2) 932 - 976, May 2019. https://doi.org/10.3150/17-BEJ1009

Information

Received: 1 January 2017; Revised: 1 November 2017; Published: May 2019
First available in Project Euclid: 6 March 2019

zbMATH: 07049396
MathSciNet: MR3920362
Digital Object Identifier: 10.3150/17-BEJ1009

Rights: Copyright © 2019 Bernoulli Society for Mathematical Statistics and Probability

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Vol.25 • No. 2 • May 2019
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