Sensitivity indices for independent groups of variables - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2019

Sensitivity indices for independent groups of variables

Résumé

In this paper, we study sensitivity indices for independent groups of variables and we look at the particular case of block-additive models. We show in this case that most of the Sobol indices are equal to zero and that Shapley effects can be estimated more efficiently. We then apply this study to Gaussian linear models, and we provide an efficient algorithm to compute the theoretical sensitivity indices. In numerical experiments, we show that this algorithm compares favourably to other existing methods. We also use the theoretical results to improve the estimation of the Shapley effects for general models, when the inputs form independent groups of variables.
Fichier principal
Vignette du fichier
PapierIndicesGroupes5_12_18.pdf (316.89 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01680687 , version 1 (11-01-2018)
hal-01680687 , version 2 (10-12-2018)
hal-01680687 , version 3 (11-04-2019)

Identifiants

Citer

Baptiste Broto, François Bachoc, Marine Depecker, Jean-Marc Martinez. Sensitivity indices for independent groups of variables. 2019. ⟨hal-01680687v3⟩
492 Consultations
634 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More