In the context of the Gaussian regression model, the package RKHSMetaMod allows to estimate a meta model by solving the ridge group sparse optimization problem based on the Reproducing Kernel Hilbert Spaces (RKHS). The obtained estimator is an additive model that satisfies the properties of the Hoeffding decomposition, and its terms estimate the terms in the Hoeffding decomposition of the unknown regression function. The estimators of the Sobol indices are deduced from the estimated meta model. This package provides an interface from R statistical computing environment to the C++ libraries Eigen and GSL. In order to speed up the execution time, almost all of the functions of the RKHSMetaMod package are written using the efficient C++ librar...
Ridge regression is a classical statistical technique that attempts to address the bias-variance tra...
The class of CUB models is commonly used by practitioners to model ordinal data, in this paper we pr...
Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introdu...
In this work, the problem of estimating a meta-model of a complex model, denoted m, is considered. T...
SparseM provides some basic R functionality for linear algebra with sparse matrices. Use of the pack...
Ce travail porte sur le problème de l'estimation d'un méta-modèle d'un modèle complexe, noté m. Le m...
SparseM provides some basic R functionality for linear algebra with sparse matrices. Use of the pack...
Sparse Hessian matrices occur often in statistics, and their fast and accurate estimation can improv...
<p>The objective of RSKR is to enforce matching intensity gradient sparsity patterns and low column ...
Abstract—Reconstruction of a function from noisy data is often formulated as a regularized optimizat...
International audienceWe propose to estimate a metamodel and the sensitivity indices of a complex mo...
Explaining the output of machine learning models with more accurately estimated Shapley values
Trust region algorithms for nonlinear optimization are commonly believed to be more stable than thei...
The theory of reproducing kernel has been recognized as a useful instrument in several areas of math...
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for a...
Ridge regression is a classical statistical technique that attempts to address the bias-variance tra...
The class of CUB models is commonly used by practitioners to model ordinal data, in this paper we pr...
Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introdu...
In this work, the problem of estimating a meta-model of a complex model, denoted m, is considered. T...
SparseM provides some basic R functionality for linear algebra with sparse matrices. Use of the pack...
Ce travail porte sur le problème de l'estimation d'un méta-modèle d'un modèle complexe, noté m. Le m...
SparseM provides some basic R functionality for linear algebra with sparse matrices. Use of the pack...
Sparse Hessian matrices occur often in statistics, and their fast and accurate estimation can improv...
<p>The objective of RSKR is to enforce matching intensity gradient sparsity patterns and low column ...
Abstract—Reconstruction of a function from noisy data is often formulated as a regularized optimizat...
International audienceWe propose to estimate a metamodel and the sensitivity indices of a complex mo...
Explaining the output of machine learning models with more accurately estimated Shapley values
Trust region algorithms for nonlinear optimization are commonly believed to be more stable than thei...
The theory of reproducing kernel has been recognized as a useful instrument in several areas of math...
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for a...
Ridge regression is a classical statistical technique that attempts to address the bias-variance tra...
The class of CUB models is commonly used by practitioners to model ordinal data, in this paper we pr...
Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introdu...