Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230741
Authors: 
Bachoc, Francois
Suvorikova, Alexandra
Loubes, Jean-Michel
Spokoiny, Vladimir
Year of Publication: 
2018
Series/Report no.: 
IRTG 1792 Discussion Paper No. 2018-030
Abstract: 
In this work, we propose to define Gaussian Processes indexed by multidimensional distributions. In the framework where the distributions can be modeled as i.i.d realizations of a measure on the set of distributions, we prove that the kernel defined as the quadratic distance between the transportation maps, that transport each distribution to the barycenter of the distributions, provides a valid covariance function. In this framework, we study the asymptotic properties of this process, proving micro ergodicity of the parameters.
Subjects: 
Gaussian Process
Kernel methods
Wasserstein Distance
JEL: 
C00
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.