In this paper we address the problem of efficient estimation of Sobol sensitivy indices. First, we focus on general functional integrals of conditional moments of the form E(ψ(E(ϕ(Y)|X))) where (X,Y) is a random vector with joint den-sity f and ψ and ϕ are functions that are differentiable enough. In particular, we show that asymptotical efficient estimation of this functional boils down to the estimation of crossed quadratic functionals. An efficient estimate of first-order sensitivity indices is then derived as a special case. We investigate its properties on several analytical functions and illustrate its interest on a reservoir engineering case
A novel approach to estimate variance based sensitivity indices for the case of correlated variables...
In this work, we develop an approach mentioned by da Veiga and Gamboa in 2013. It consists in extend...
The so-called polynomial chaos expansion is widely used in computer experiments. For example, it is ...
41 pagesIn this paper we address the problem of efficient estimation of Sobol sensitivy indices. Fir...
Let X: = (X1,..., Xp) be random objects (the inputs), defined on some probability space (Ω,F,P) and ...
International audienceThe hierarchically orthogonal functional decomposition of any measurable funct...
The hierarchically orthogonal functional decomposition of any measurable function η of a random vect...
Sobol sensitivity indices assess how the output of a given mathematical model is sensitive to its i...
We use the Karhunen-Loève expansion of a random-field model to construct a tensorised Bayesian linea...
International audienceIn this paper, we first study a new sensitivity index that is based on higher ...
Many mathematical models involve input parameters, which are not precisely known. Global s...
In this paper, we study sensitivity indices for independent groups of variables and we look at the p...
The estimation of variance-based importance measures (called Sobol' indices) of the input variables ...
We consider a functional linear model where the explicative variables are stochastic processes takin...
A novel theoretical and numerical framework for the estimation of Sobol sensitivity indices for mode...
A novel approach to estimate variance based sensitivity indices for the case of correlated variables...
In this work, we develop an approach mentioned by da Veiga and Gamboa in 2013. It consists in extend...
The so-called polynomial chaos expansion is widely used in computer experiments. For example, it is ...
41 pagesIn this paper we address the problem of efficient estimation of Sobol sensitivy indices. Fir...
Let X: = (X1,..., Xp) be random objects (the inputs), defined on some probability space (Ω,F,P) and ...
International audienceThe hierarchically orthogonal functional decomposition of any measurable funct...
The hierarchically orthogonal functional decomposition of any measurable function η of a random vect...
Sobol sensitivity indices assess how the output of a given mathematical model is sensitive to its i...
We use the Karhunen-Loève expansion of a random-field model to construct a tensorised Bayesian linea...
International audienceIn this paper, we first study a new sensitivity index that is based on higher ...
Many mathematical models involve input parameters, which are not precisely known. Global s...
In this paper, we study sensitivity indices for independent groups of variables and we look at the p...
The estimation of variance-based importance measures (called Sobol' indices) of the input variables ...
We consider a functional linear model where the explicative variables are stochastic processes takin...
A novel theoretical and numerical framework for the estimation of Sobol sensitivity indices for mode...
A novel approach to estimate variance based sensitivity indices for the case of correlated variables...
In this work, we develop an approach mentioned by da Veiga and Gamboa in 2013. It consists in extend...
The so-called polynomial chaos expansion is widely used in computer experiments. For example, it is ...