Arbeitspapier
Gaussian Process Forecast with multidimensional distributional entries
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: IRTG 1792 Discussion Paper ; No. 2018-030
- Klassifikation
-
Wirtschaft
Mathematical and Quantitative Methods: General
- Thema
-
Gaussian Process
Kernel methods
Wasserstein Distance
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Bachoc, Francois
Suvorikova, Alexandra
Loubes, Jean-Michel
Spokoiny, Vladimir
- Ereignis
-
Veröffentlichung
- (wer)
-
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
- (wo)
-
Berlin
- (wann)
-
2018
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Bachoc, Francois
- Suvorikova, Alexandra
- Loubes, Jean-Michel
- Spokoiny, Vladimir
- Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
Entstanden
- 2018
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