Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Taupin Marie-Luce"'
Autor:
Frioux Clémence, Huet Sylvie, Labarthe Simon, Martinelli Julien, Malou Thibault, Sherman David, Taupin Marie-Luce, Ugalde-Salas Pablo
Publikováno v:
ESAIM: Proceedings and Surveys, Vol 73, Pp 187-217 (2023)
Mathematical and numerical models are increasingly used in microbial ecology to model the fate of microbial communities in their ecosystem. These models allow to connect in a mechanistic framework species-level informations, such as the microbial gen
Externí odkaz:
https://doaj.org/article/4b33d1c53b4047db89663589b190ab8b
Autor:
Huet Sylvie, Taupin Marie-Luce
Publikováno v:
ESAIM: Proceedings and Surveys, Vol 60, Pp 27-69 (2017)
We propose to estimate a metamodel and the sensitivity indices of a complex model m in the Gaussian regression framework. Our approach combines methods for sensitivity analysis of complex models and statistical tools for sparse non-parametric estimat
Externí odkaz:
https://doaj.org/article/1b0955a59d2244c68af6e09c79e249d2
We consider the problem of estimating a meta-model of an unknown regression model with non-Gaussian and non-bounded error. The meta-model belongs to a reproducing kernel Hilbert space constructed as a direct sum of Hilbert spaces leading to an additi
Externí odkaz:
http://arxiv.org/abs/2009.11646
In this paper, we propose an R package, called RKHSMetaMod, that implements a procedure for estimating a meta-model of a complex model. The meta-model approximates the Hoeffding decomposition of the complex model and allows us to perform sensitivity
Externí odkaz:
http://arxiv.org/abs/1905.13695
Autor:
Savall, Jordi Ferrer, Franqueville, Damien, Barbillon, Pierre, Benhamou, Cyril, Durand, Patrick, Taupin, Marie-Luce, Monod, Hervé, Drouet, Jean-Louis
Modelling complex systems such as agroecosystems often requires the quantification of a large number of input factors. Sensitivity analyses are useful to determine the appropriate spatial and temporal resolution of models and to reduce the number of
Externí odkaz:
http://arxiv.org/abs/1709.08608
Autor:
Huet, Sylvie, Taupin, Marie-Luce
We propose to estimate a metamodel and the sensitivity indices of a complex model m in the Gaussian regression framework. Our approach combines methods for sensitivity analysis of complex models and statistical tools for sparse non-parametric estimat
Externí odkaz:
http://arxiv.org/abs/1701.04671
This paper is devoted to model selection in logistic regression. We extend the model selection principle introduced by Birg\'e and Massart (2001) to logistic regression model. This selection is done by using penalized maximum likelihood criteria. We
Externí odkaz:
http://arxiv.org/abs/1508.07537
The aim of this article is to propose a novel kernel estimator of the baseline function in a general high-dimensional Cox model, for which we derive non-asymptotic rates of convergence. To construct our estimator, we first estimate the regression par
Externí odkaz:
http://arxiv.org/abs/1507.01397
The purpose of this article is to provide an adaptive estimator of the baseline function in the Cox model with high-dimensional covariates. We consider a two-step procedure : first, we estimate the regression parameter of the Cox model via a Lasso pr
Externí odkaz:
http://arxiv.org/abs/1503.00226
Consider an autoregressive model with measurement error: we observe $Z_i=X_i+\epsilon_i$, where $X_i$ is a stationary solution of the equation $X_i=f_{\theta^0}(X_{i-1})+\xi_i$. The regression function $f_{\theta^0}$ is known up to a finite dimension
Externí odkaz:
http://arxiv.org/abs/1105.1310