L2-Boosting for sensitivity analysis with dependent inputs | Zendy

Magali Champion | Zendy; Gaëlle Chastaing | Zendy; Sébastien Gadat | Zendy; Cl ́ementine Prieur | Zendy

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L2-Boosting for sensitivity analysis with dependent inputs

Author(s) -

Magali Champion,

Gaëlle Chastaing,

Sébastien Gadat,

Cl ́ementine Prieur

Publication year - 2014

Publication title -

statistica sinica

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.24

H-Index - 77

eISSN - 1996-8507

pISSN - 1017-0405

DOI - 10.5705/ss.2013.310

Subject(s) - estimator , boosting (machine learning) , consistency (knowledge bases) , sensitivity (control systems) , benchmark (surveying) , mathematics , mathematical optimization , computer science , algorithm , statistics , artificial intelligence , electronic engineering , engineering , geodesy , geography

International audienceThis paper is dedicated to the study of an estimator of the generalized Hoeffding decomposition. We build such an estimator using an empirical Gram-Schmidt approach and derive a consistency rate in a large dimensional setting. We then apply a greedy algorithm with these previous estimators to a sensitivity analysis. We also establish the consistency of this L2-boosting under sparsity assumptions of the signal to be analyzed. The paper concludes with numerical experiments, that demonstrate the low computational cost of our method, as well as its efficiency on the standard benchmark of sensitivity analysis

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