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November 2018 Wide consensus aggregation in the Wasserstein space. Application to location-scatter families
Pedro C. Álvarez-Esteban, Eustasio del Barrio, Juan A. Cuesta-Albertos, Carlos Matrán
Bernoulli 24(4A): 3147-3179 (November 2018). DOI: 10.3150/17-BEJ957


We introduce a general theory for a consensus-based combination of estimations of probability measures. Potential applications include parallelized or distributed sampling schemes as well as variations on aggregation from resampling techniques like boosting or bagging. Taking into account the possibility of very discrepant estimations, instead of a full consensus we consider a “wide consensus” procedure. The approach is based on the consideration of trimmed barycenters in the Wasserstein space of probability measures. We provide general existence and consistency results as well as suitable properties of these robustified Fréchet means. In order to get quick applicability, we also include characterizations of barycenters of probabilities that belong to (non necessarily elliptical) location and scatter families. For these families, we provide an iterative algorithm for the effective computation of trimmed barycenters, based on a consistent algorithm for computing barycenters, guarantying applicability in a wide setting of statistical problems.


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Pedro C. Álvarez-Esteban. Eustasio del Barrio. Juan A. Cuesta-Albertos. Carlos Matrán. "Wide consensus aggregation in the Wasserstein space. Application to location-scatter families." Bernoulli 24 (4A) 3147 - 3179, November 2018.


Received: 1 October 2016; Revised: 1 May 2017; Published: November 2018
First available in Project Euclid: 26 March 2018

zbMATH: 06853276
MathSciNet: MR3779713
Digital Object Identifier: 10.3150/17-BEJ957

Rights: Copyright © 2018 Bernoulli Society for Mathematical Statistics and Probability


Vol.24 • No. 4A • November 2018
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