François Bachoc
François Bachoc
Associate professor, Toulouse Mathematics Institute
Adresse e-mail validée de math.univ-toulouse.fr - Page d'accueil
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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
1452013
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
372014
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
342014
A supermartingale approach to Gaussian process based sequential design of experiments
J Bect, F Bachoc, D Ginsbourger
Bernoulli 25 (4A), 2883-2919, 2019
312019
Valid confidence intervals for post-model-selection predictors
F Bachoc, H Leeb, BM Pötscher
arXiv preprint arXiv:1412.4605, 2014
25*2014
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
242018
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
212017
Asymptotic properties of multivariate tapering for estimation and prediction
R Furrer, F Bachoc, J Du
Journal of Multivariate Analysis 149, 177-191, 2016
212016
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
192018
Uniformly valid confidence intervals post-model-selection
F Bachoc, D Preinerstorfer, L Steinberger
The Annals of Statistics 48 (1), 440-463, 2020
182020
Asymptotic analysis of covariance parameter estimation for Gaussian processes in the misspecified case
F Bachoc
Bernoulli 24 (2), 1531-1575, 2018
172018
Parametric estimation of covariance function in Gaussian-process based Kriging models. Application to uncertainty quantification for computer experiments
F Bachoc
172013
Estimation paramétrique de la fonction de covariance dans le modèle de krigeage par processus gaussiens: application à la quantification des incertitues en simulation numérique
F Bachoc
Paris 7, 2013
172013
Sensitivity indices for independent groups of variables
B Broto, F Bachoc, M Depecker, JM Martinez
Mathematics and Computers in Simulation 163, 19-31, 2019
112019
Gaussian process forecast with multidimensional distributional entries
F Bachoc, A Suvorikova, JM Loubes, V Spokoiny
arXiv preprint arXiv:1805.00753, 2018
9*2018
Cross-validation estimation of covariance parameters under fixed-domain asymptotics
F Bachoc, A Lagnoux, TMN Nguyen
Journal of Multivariate Analysis 160, 42-67, 2017
82017
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
72019
On the post selection inference constant under restricted isometry properties
F Bachoc, G Blanchard, P Neuvial
Electronic Journal of Statistics 12 (2), 3736-3757, 2018
72018
Improvement of code behavior in a design of experiments by metamodeling
F Bachoc, K Ammar, JM Martinez
Nuclear science and engineering 183 (3), 387-406, 2016
72016
Sequential dimension reduction for learning features of expensive black-box functions
MB Salem, F Bachoc, O Roustant, F Gamboa, L Tomaso
62019
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