4 citations · 6 across the 12 of their papers we have counts for
12 papers
Sharp inequalities between variance-based dependent sensitivity indices and Shapley effects: upper-bounds
Matieyendou Lamboni
For models evaluated at a random set of independent variables, the variance-based Shapley effects range between Sobol' indices, and the corresponding total indices admit derivative…
Active subspace methods and derivative-based Shapley effects for functions with non-independent variables
Matieyendou Lamboni, Sergei Kucherenko
Lower-dimensional subspaces that impact estimates of uncertainty are often described by Linear combinations of input variables, leading to active variables. This paper extends the…
Dimension-free estimators of gradients of functions with(out) non-independent variables
Matieyendou Lamboni
This study proposes a unified stochastic framework for approximating and computing the gradient of every smooth function evaluated at non-independent variables, using -sphe…
Optimal ANOVA-based emulators of models with(out) derivatives
Matieyendou Lamboni
This paper proposes new ANOVA-based approximations of functions and emulators of high-dimensional models using either available derivatives or local stochastic evaluations of such…
Optimal estimators of cross-partial derivatives and surrogates of functions
Matieyendou Lamboni
Computing cross-partial derivatives using fewer model runs is relevant in modeling, such as stochastic approximation, derivative-based ANOVA, exploring complex models, and active s…
Kernel-based measures of association between inputs and outputs based on ANOVA
Matieyendou Lamboni
ANOVA decomposition of function with random input variables provides ANOVA functionals (AFs), which contain information about the contributions of the input variables on the output…
Kernel-based sensitivity indices for any model behavior and screening
Matieyendou Lamboni
Complex models are often used to understand interactions and drivers of human-induced and/or natural phenomena. It is worth identifying the input variables that drive the model out…
Gradient and Hessian of functions with non-independent variables
Matieyendou Lamboni
Mathematical models are sometime given as functions of independent input variables and equations or inequations connecting the input variables. A probabilistic characterization of…
Efficient dependency models for some distributions
Matieyendou Lamboni
Dependency functions of dependent variables are relevant for i) performing uncertainty quantification and sensitivity analysis in presence of dependent variables and/or correlated…
On dependency models and dependent generalized sensitivity indices
Matieyendou Lamboni
In this paper, we derive copula-based and empirical dependency models (DMs) for simulating non-independent variables, and then propose a new way for determining the distribution of…
Integral equalities and inequalities: a proxy-measure for multivariate sensitivity analysis
Matieyendou Lamboni
Weighted Poincaré-type and related inequalities provide upper bounds of the variance of functions. Their application in sensitivity analysis allows for quickly identifying the acti…
Derivative-based global sensitivity measures: general links with Sobol' indices and numerical tests
Matieyendou Lamboni, Bertrand Iooss, Anne-Laure Popelin +1
The estimation of variance-based importance measures (called Sobol' indices) of the input variables of a numerical model can require a large number of model evaluations. It turns t…