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pro vyhledávání: '"Broto, Baptiste"'
In this paper, we address the estimation of the sensitivity indices called "Shapley eects". These sensitivity indices enable to handle dependent input variables. The Shapley eects are generally dicult to estimate, but they are easily computable in th
Externí odkaz:
http://arxiv.org/abs/2006.02087
In this paper, we aim to estimate block-diagonal covariance matrices for Gaussian data in high dimension and in fixed dimension. We first estimate the block-diagonal structure of the covariance matrix by theoretical and practical estimators which are
Externí odkaz:
http://arxiv.org/abs/1907.12780
The Shapley effects are global sensitivity indices: they quantify the impact of each input variable on the output variable in a model. In this work, we suggest new estimators of these sensitivity indices. When the input distribution is known, we inve
Externí odkaz:
http://arxiv.org/abs/1812.09168
In the framework of the supervised learning of a real function defined on a space X , the so called Kriging method stands on a real Gaussian field defined on X. The Euclidean case is well known and has been widely studied. In this paper, we explore t
Externí odkaz:
http://arxiv.org/abs/1803.06118
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 estimat
Externí odkaz:
http://arxiv.org/abs/1801.04095
Akademický článek
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Autor:
Broto, Baptiste
Publikováno v:
Statistics [math.ST]. Université Paris-Saclay, 2020. English. ⟨NNT : 2020UPASS119⟩
Sensitivity analysis is a powerful tool to study mathematical models and computer codes. It reveals the most impacting input variables on the output variable, by assigning values to the the inputs, that we call "sensitivity indices". In this setting,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::508dbf696b7d30422806d90415eb8706
https://tel.archives-ouvertes.fr/tel-02976702
https://tel.archives-ouvertes.fr/tel-02976702
Autor:
Broto, Baptiste
Publikováno v:
Statistics [math.ST]. Université Paris-Saclay, 2020. English. ⟨NNT : 2020UPASS119⟩
Sensitivity analysis is a powerful tool to study mathematical models and computer codes. It reveals the most impacting input variables on the output variable, by assigning values to the the inputs, that we call "sensitivity indices". In this setting,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______212::508dbf696b7d30422806d90415eb8706
https://tel.archives-ouvertes.fr/tel-02976702
https://tel.archives-ouvertes.fr/tel-02976702
In the framework of the supervised learning of a real function defined on a space X , the so called Kriging method stands on a real Gaussian field defined on X. The Euclidean case is well known and has been widely studied. In this paper, we explore t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4fab58741abf00f851b83b20e99675a2
https://hal.archives-ouvertes.fr/hal-01731251v5/file/permutations_final.pdf
https://hal.archives-ouvertes.fr/hal-01731251v5/file/permutations_final.pdf
Akademický článek
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