Nested Kriging predictions for datasets with a large number of observations

Abstract : This work falls within the context of predicting the value of a real function at some input locations given a limited number of observations of this function. The Kriging interpolation technique (or Gaussian process regression) is often considered to tackle such a problem, but the method suffers from its computational burden when the number of observation points is large. We introduce in this article nested Kriging predictors which are constructed by aggregating sub-models based on subsets of observation points. This approach is proven to have better theoretical properties than other aggregation methods that can be found in the literature. Contrarily to some other methods it can be shown that the proposed aggregation method is consistent. Finally, the practical interest of the proposed method is illustrated on simulated datasets and on an industrial test case with (Formula presented.) observations in a 6-dimensional space.
Type de document :
Article dans une revue
Statistics and Computing, Springer Verlag (Germany), 2017, pp 1-19. 〈10.1007/s11222-017-9766-2〉
Liste complète des métadonnées

https://hal-emse.ccsd.cnrs.fr/emse-01576391
Contributeur : Florent Breuil <>
Soumis le : mercredi 23 août 2017 - 09:21:24
Dernière modification le : vendredi 14 septembre 2018 - 09:16:05

Identifiants

Citation

Didier Rullière, Nicolas Durrande, François Bachoc, Clément Chevalier. Nested Kriging predictions for datasets with a large number of observations. Statistics and Computing, Springer Verlag (Germany), 2017, pp 1-19. 〈10.1007/s11222-017-9766-2〉. 〈emse-01576391〉

Partager

Métriques

Consultations de la notice

331