| Cross validation and maximum likelihood estimations of hyper-parameters of Gaussian processes with model misspecification F Bachoc Computational Statistics & Data Analysis 66, 55-69, 2013 | 214 | 2013 |
| A supermartingale approach to Gaussian process based sequential design of experiments J Bect, F Bachoc, D Ginsbourger Bernoulli 25 (4A), 2883-2919, 2019 | 62 | 2019 |
| Finite-dimensional Gaussian approximation with linear inequality constraints AF López-Lopera, F Bachoc, N Durrande, O Roustant SIAM/ASA Journal on Uncertainty Quantification 6 (3), 1224-1255, 2018 | 57 | 2018 |
| Nested Kriging predictions for datasets with a large number of observations D Rullière, N Durrande, F Bachoc, C Chevalier Statistics and Computing 28 (4), 849-867, 2018 | 52 | 2018 |
| Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes F Bachoc Journal of Multivariate Analysis 125, 1-35, 2014 | 50 | 2014 |
| Valid confidence intervals for post-model-selection predictors F Bachoc, H Leeb, BM Pötscher The Annals of Statistics 47 (3), 1475-1504, 2019 | 42* | 2019 |
| A Gaussian process regression model for distribution inputs F Bachoc, F Gamboa, JM Loubes, N Venet IEEE Transactions on Information Theory 64 (10), 6620-6637, 2017 | 42 | 2017 |
| Calibration and improved prediction of computer models by universal Kriging F Bachoc, G Bois, J Garnier, JM Martinez Nuclear Science and Engineering 176 (1), 81-97, 2014 | 42 | 2014 |
| Uniformly valid confidence intervals post-model-selection F Bachoc, D Preinerstorfer, L Steinberger The Annals of Statistics 48 (1), 440-463, 2020 | 38 | 2020 |
| Parametric estimation of covariance function in Gaussian-process based Kriging models. Application to uncertainty quantification for computer experiments F Bachoc Université Paris-Diderot-Paris VII, 2013 | 29 | 2013 |
| Asymptotic analysis of covariance parameter estimation for Gaussian processes in the misspecified case F Bachoc Bernoulli 24 (2), 1531-1575, 2018 | 27 | 2018 |
| Asymptotic properties of multivariate tapering for estimation and prediction R Furrer, F Bachoc, J Du Journal of Multivariate Analysis 149, 177-191, 2016 | 26 | 2016 |
| Estimation paramétrique de la fonction de covariance dans le modèle de krigeage par processus gaussiens: application à la quantification des incertitudes en simulation numérique F Bachoc Paris 7, 2013 | 24 | 2013 |
| Maximum likelihood estimation for Gaussian processes under inequality constraints F Bachoc, A Lagnoux, AF López-Lopera Electronic Journal of Statistics 13 (2), 2921-2969, 2019 | 23 | 2019 |
| Variance reduction for estimation of Shapley effects and adaptation to unknown input distribution B Broto, F Bachoc, M Depecker SIAM/ASA Journal on Uncertainty Quantification 8 (2), 693-716, 2020 | 22 | 2020 |
| Sensitivity indices for independent groups of variables B Broto, F Bachoc, M Depecker, JM Martinez Mathematics and Computers in Simulation 163, 19-31, 2019 | 19 | 2019 |
| Gaussian process metamodeling of functional-input code for coastal flood hazard assessment J Betancourt, F Bachoc, T Klein, D Idier, R Pedreros, J Rohmer Reliability Engineering & System Safety 198, 106870, 2020 | 18 | 2020 |
| Cross-validation estimation of covariance parameters under fixed-domain asymptotics F Bachoc, A Lagnoux, TMN Nguyen Journal of Multivariate Analysis 160, 42-67, 2017 | 17 | 2017 |
| Gaussian process optimization with failures: classification and convergence proof F Bachoc, C Helbert, V Picheny Journal of Global Optimization 78 (3), 483-506, 2020 | 16 | 2020 |
| Spatial blind source separation F Bachoc, MG Genton, K Nordhausen, A Ruiz-Gazen, J Virta Biometrika 107 (3), 627-646, 2020 | 16 | 2020 |
Fabrice GamboaIMTVerified email at math.univ-toulouse.fr
