Wide consensus aggregation in the Wasserstein space. Application to location-scatter families
| Autor: | Pedro C. Alvarez-Esteban, Eustasio del Barrio, Juan A. Cuesta-Albertos, Carlos Matrán |
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| Přispěvatelé: | Universidad de Cantabria |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
Boosting (machine learning) Iterative method Robust aggregation Computation trimmed distributions Trimmed distributions Space (mathematics) 01 natural sciences wide consensus Parallelized inference Wide consensus 010104 statistics & probability Consistency (statistics) Resampling Trimmed barycenter Wasserstein distance 0101 mathematics impartial trimming Mathematics Probability measure 010102 general mathematics Sampling (statistics) robust aggregation trimmed barycenter Impartial trimming parallelized inference Algorithm |
| Zdroj: | Bernoulli 24, no. 4A (2018), 3147-3179 Bernoulli 24(4A), 2018, 3147-3179 UCrea Repositorio Abierto de la Universidad de Cantabria Universidad de Cantabria (UC) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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