Homepage of Clémentine Prieur

Miscellaneous

C. Prieur (2015),
Mieux modéliser le climat grâce aux statistiques.
Podcast Interstices lien

Book

S. Da Veiga, F. Gamboa, B. Iooss, C. Prieur (2021),
Basics and Trends in Sensitivity Analysis: Theory and Practice in R.
SIAM publisher.
Computational science and engineering.
J. Dedecker, P. Doukhan, G. Lang, J.R. Léon, S. Louhichi, C. Prieur (2007),
Weak Dependance: With Examples and Applications.
Springer, New York.
Lecture Notes in Statistics 190. lien

Book chapters

C. Prieur and S. Tarantola (2017),
Variance-based sensitivity analysis: Theory and estimation algorithms.
In: Springer Handbook on UQ, R. Ghanem, D. Higdon and H. Owhadi (Eds), p. 1217-1239.

C. Prieur (2006),
Recent results on weak dependence for causal sequences. Statistical applications to dynamical systems.
Lect. notes in Stat., Springer, Bertail, Patrice; Doukhan, Paul; Soulier, Philippe (Eds.), Vol. 187. ps

J. Dedecker, C. Prieur and P. Raynaud de Fitte (2006),
Parametrized Kantorovich-Rubinstein Theorem and application to the coupling of random variables.
Lect. notes in Stat., Springer, Bertail, Patrice; Doukhan, Paul; Soulier, Philippe (Eds.), Vol. 187. ps

Special issue

B. Iooss, B. Sudret, S. Lo Piano and C. Prieur
Special Issue on Sensitivity Analysis of Model Outputs.
Reliability Engineering and System Safety, 10477, 2022. link to Editorial

C. Prieur, A. Pasanisi et F. Wahl (2011),
Méthodes stochastiques pour l'analyse de sensibilité. (French) [Stochastic methods for sensitivity analysis]
J. SFdS, Volume 152, Issue 1. lien

Journal papers

H. M. Kouye, C. Prieur and E. Vergu (2025),
An extension of Sellke construction and uncertainty quantification for non-Markovian epidemic models
Accepted in Mathematical Modelling of Natural Phenomena, hal

R. Vaudry, C. Sonveaux, D. Georges and C. Prieur (2025),
An integro-ODE-PDE model for Covid-19 pandemic with vaccination and loss of immunity: well-posedness, RBF-FD numerical scheme and simulation.
Ima Journal Of Mathematical Control And Information, doi hal

R. Verdière, C. Prieur and O. Zahm (2025),
Diffeomorphism-based feature learning using Poincaré Inequalities on augmented input space
Journal of Machine Learning Research 26(139):1−31. , hal

H. Mermoz Kouye, G. Mazo, C. Prieur and E. Vergu (2024),
Performing global sensitivity analysis on simulations of a continuous-time Markov chain model motivated by epidemiology
Computational and Applied Mathematics, 43, 409 hal

E. Borgonovo, E. Plischke and C. Prieur (2024),
Total Effects with Constrained Features.
Statistics and Computing, Vol. 34, No. 87 link

M. Billaud-Friess, A. Macherey, A. Nouy and C. Prieur (2024),
A probabilistic reduced basis method for parameter-dependent problems
Advances in Computational Mathematics, Vol. 50, No. 19 arXiv

M. R. El Amri, C. Helbert, M. Munoz Zuniga, C. Prieur and D. Sinoquet (2023),
Feasible set estimation under functional uncertainty by Gaussian Process modelling.
Physica D: Nonlinear Phenomena, Vol. 455, 133893 hal

P. Etoré, J. R. León and C. Prieur (2023),
A probabilistic point of view for the exact or approximated computation of the solution to Kolmogorov hypoelliptic equations.
ESAIM: Probability and Statistics, Vol. 27, 668–693 doi hal

C. Duhamel, C. Helbert, M. Munoz Zuniga, C. Prieur and D. Sinoquet (2023),
A SUR version of the Bichon criterion for excursion set estimation.
Statistics and Computing, Vol. 33, No. 2, 41 hal

A. Hirvoas, C. Prieur, E. Arnaud, F. Caleyron and M. Munoz Zuniga (2022),
Wind turbine quantification and reduction of uncertainties based on a data-driven data assimilation approach.
Journal of Renewable and Sustainable Energy, Vol. 14, No. 5 doi hal

P. Le Gall, A.-C. Favre, P. Naveau and C. Prieur (2022),
Improved Regional Frequency Analysis of rainfall data
Weather and Climate Extremes, Vol. 36, 100456 doi hal

M. Oliver, D. Georges and C. Prieur (2022),
Spatialized Epidemiological Forecasting applied to Covid-19 Pandemic at Departmental Scale in France
Systems & Control Letters, Vol. 164, 105240
doi hal

D. Bigoni, Y. Marzouk, C. Prieur and O. Zahm (2022),
Nonlinear dimension reduction for surrogate modeling using gradient information.
Information and Inference: A Journal of the IMA, Vol. 11 (4), 1597-1639 arXiv

M. B. Heredia, C. Prieur and N. Eckert (2022),
Global sensitivity analysis with aggregated Shapley effects, application to avalanche hazard assessment.
Reliability Engineering & System Safety, Vol. 222, 108420 hal

M. Billaud-Friess, A. Macherey, A. Nouy and C. Prieur (2022),
A PAC algorithm in relative precision for bandit problem with costly sampling
Mathematical Methods of Operations Research, Vol. 96, 161-185 arXiv

M. B. Heredia, C. Prieur and N. Eckert (2021),
Nonparametric estimation of aggregated Sobol' indices: application to a depth averaged snow avalanche model.
Reliability Engineering & System Safety, Vol. 212, p. 107422 hal

A. Hirvoas, C. Prieur, E. Arnaud, F. Caleyron and M. Munoz Zuniga (2021),
Quantification and reduction of uncertainties in a wind turbine numerical model based on a global sensitivity analysis and a recursive Bayesian inference approach.
International Journal for Numerical Methods in Engineering, Vol. 122 (10), p. 2528-2544 hal

L. Gilquin, E. Arnaud, C. Prieur and H. Monod (2021),
Recursive estimation procedure of Sobol' indices based on replicated designs.
Computational and Applied Mathematics, Vol. 40 (1), p. 1-23 doi hal

S. Razavi, A. Jakeman, A. Saltelli, C. Prieur, B. Iooss et al. (2021),
Position Paper:
The Future of Sensitivity Analysis: An Essential Discipline for Systems Modelling and Policy Making.
Environmental Modelling and Software, Vol. 137, p. 104954 hal

L. Guan, Ch. Prieur, L. Zhang, Cl. Prieur, D. Georges and P. Bellemain (2020),
Transport effect of COVID-19 pandemic in France.
Annual Reviews in Control, Vol. 50, p. 394-408 hal

K. Bertin, J. R. León, N. Klutchnikoff and C. Prieur (2020),
Adaptive density estimation on bounded domains under mixing conditions.
Electronic Journal of Statistics Vol. 14, p. 2198-2237 hal

M. B. Heredia, N. Eckert, C. Prieur and E. Thibert (2020),
Bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to high rate photogrammetric image.
Journal of Glaciology Vol. 66 (257), p. 373-385.

O. Zahm, P. Constantine, C. Prieur and Y. Marzouk (2020),
Gradient-based dimension reduction of multivariate vector-valued functions.
SIAM Journal on Scientific Computing Vol. 42 (1): A534-A558 arXiv

M. R. El Amri, C. Helbert, O. Lepreux, M. Munoz Zuniga, C. Prieur and D. Sinoquet (2020),
Data-driven stochastic inversion via functional quantization.
Statistics and Computing Vol. 30 (3), p. 525-541 hal

P. Etoré, C. Prieur, D. K. Pham and L. Li (2020),
Global sensitivity analysis for models described by stochastic differential equations.
Methodology and Computing in Applied Probability Vol. 22, p. 803-831 hal

P. Tencaliec, A.-C. Favre, P. Naveau, C. Prieur and G. Nicolet (2020),
Flexible semiparametric Generalized Pareto modeling of the entire range of rainfall amount.
Environmetrics Vol. 31 (2): e2582 hal

C. Prieur, L. Viry, E. Blayo and J.-M. Brankart (2019),
A global sensitivity analysis approach for marine biogeochemical modeling.
Ocean Modelling, Vol. 139, 101402 hal

B. Iooss and C. Prieur (2019),
Shapley effects for sensitivity analysis with correlated inputs: comparisons with Sobol' indices, numerical estimation and applications.
International Journal for Uncertainty Quantification Volume 9 (5), p. 493-514 hal

L. Gilquin, E. Arnaud, C. Prieur and A. Janon (2019),
Making the best use of permutations to compute sensitivity indices with replicated orthogonal arrays.
Reliability Engineering & System Safety 187, p. 28-39 hal

M. B. Heredia, C. Junquas, C. Prieur, T. Condom (2018),
New statistical methods for precipitation bias correction applied to WRF model simulations in the Antisana Region (Ecuador).
Journal of Hydrometeorology, 19 (12), p. 2021-2040.

E. Di Bernardino and C. Prieur (2018),
Estimation of the Multivariate Conditional-Tail-Expectation for extreme risk levels: illustration on environmental data-sets.
Environmetrics, 29 (7), p. e2510 hal

Janon, A., Nodet, M., Prieur, C. and Prieur, C. (2018),
Goal-oriented error estimation for parameter-dependent nonlinear problems.
ESAIM: Mathematical Modelling and Numerical Analysis, 52 (2), p. 705-728 hal

Owen, A. B. and Prieur, C. (2017),
On Shapley value for measuring importance of dependent inputs.
SIAM/ASA Journal on Uncertainty Quantification, 5 (1), p.986-1002 arXiv errata

Comte, F., Prieur, C. and Samson, A. (2017),
Adaptive estimation for Stochastic Damping Hamiltonian Systems under Partial Observation. Stochastic Processes and their Applications, 127 (11), p. 3689-3718 hal

P. Cattiaux, J. R. León and C. Prieur (2017),
Invariant density estimation for a reflected diffusion using an Euler scheme.
Monte Carlo Methods and Applications, 23 (2), p. 71-88 hal

L. Gilquin, T. Capelle, E. Arnaud and C. Prieur (2017),
Sensitivity Analysis and Optimisation of a Land Use and Transport Integrated Model.
Journal de la Société Française de Statistique, numéro spécial Computer experiments, 158 (1), p. 90-110 hal

P. Cattiaux, J. R. León, A. A. Pineda and C. Prieur (2017),
An overlook on statistical inference issues for stochastic damping hamiltonian systems under the fluctuation-dissipation condition.
Statistics, special issue dedicated to the conference held in Paris, May 11-13, 2015 to honor Paul Doukhan, 51 (1), p. 11-29 hal

Gilquin, L., Jiménez Rugama, L. A., Arnaud, E., Hickernell, F. J., Monod, H. and Prieur, C. (2017),
Iterative construction of replicated designs based on Sobol' sequences.
C. R. Acad. Sci. Paris, Série 1, 355 (1), p. 10-14.

S. Nanty, C. Helbert, A. Marrel, N. Pérot and C. Prieur (2017),
Uncertainy quantication for functional dependent random variables.
Computational Statistics, 32 (2), p. 559-583.

S. Nanty, C. Helbert, A. Marrel, N. Pérot and C. Prieur (2016),
Sampling, metamodelling and sensitivity analysis of numerical simulators with functional stochastic inputs.
SIAM/ASA Journal on Uncertainty Quantification, 4 (1), p. 636-659.

A. Janon, M. Nodet, C. Prieur and C. Prieur (2016),
Global sensitivity analysis for the boundary control of an open channel.
Math. Control Signals Systems, 28 (1), p. 1-27.

A. Janon, M. Nodet, C. Prieur (2016),
Goal-oriented error estimation for the reduced basis method, with application to sensitivity analysis.
Journal of Scientific Computing, 68 (1), p. 21-41 hal

F. Gamboa, A. Janon, T. Klein, A. Lagnoux, C. Prieur (2016),
Statistical inference for Sobol pick freeze Monte Carlo method.
Statistics, 50 (4), p. 881-902 hal

P. Cattiaux, J. R. León and C. Prieur (2016),
Estimation for Stochastic Damping Hamiltonian Systems under Partial Observation. III. Diffusion term.
Annals of Applied Probability, 26 (3), p. 1581-1619 hal

A. Makris, C. Prieur, T. Vischel, G. Quantin, T. Lebel and R. Roca (2016),
Stochastic Tracking of Mesoscale Convective Systems: Evaluation in the West African Sahel.
Stochastic Environmental Research and Risk Assessment, 30 (2), p. 681-691.

P. Tencaliec, A.-C. Favre, C. Prieur and T. Mathevet (2015),
Reconstruction of missing daily streamflow data using dynamic regression models.
Water Resour. Res., 51 (12), p. 9447-9463.

L. Gilquin, C. Prieur and E. Arnaud (2015),
Replication procedure for grouped Sobol' indices estimation in dependent uncertainty spaces.
Information and Inference: A Journal of the IMA, 4 (4), p. 354-379, with the reproducible flag hal

P. Cattiaux, J. R. León and C. Prieur (2015),
Recursive Estimation for Stochastic Damping Hamiltonian Systems.
Journal of Nonparametric Statistics, Volume 27, no. 3, pages 401-424 hal

J.-Y. Tissot and C. Prieur (2015),
A randomized Orthogonal Array-based procedure for the estimation of first- and second-order Sobol' indices.
Journal of Statistical Computation and Simulation, Volume 85, Issue 7, pages 1358-1381 hal

M. Champion, G. Chastaing, S. Gadat and C. Prieur (2015),
L2 -Boosting for sensitivity analysis with dependent inputs.
Statistica Sinica, 25 (4), p. 1477-1502 hal

G. Chastaing, F. Gamboa, C. Prieur (2015),
Generalized Sobol sensitivity indices for dependent variables: numerical methods.
Journal of Statistical Computation and Simulation, Volume 85, Issue 7, pages 1306-1333 hal

P. Cénac, S. Loisel, V. Maume-Deschamps, C. Prieur (2014),
Ann. ISUP , Volume 58, Issue 3, pages 3-26 version preprint

P. Cattiaux, J. R. León and C. Prieur (2014),
Estimation for Stochastic Damping Hamiltonian Systems under Partial Observation. II. Drift term.
ALEA, Volume 11, Issue 1, pages 359-384 hal

A. Makris and C. Prieur (2014),
Bayesian Multiple Hypothesis Tracking of Merging and Splitting Targets.
IEEE Transactions on Geoscience and Remote Sensing Volume 52, Issue 12, pages 7684-7694 hal

E. Di Bernardino and C. Prieur (2014),
Estimation of Multivariate Conditional Tail Expectation using Kendall's Process.
Journal of Nonparametric Statistics Volume 26, Issue 2, pages 241-267 hal

P. Cattiaux, J. R. León and C. Prieur (2014),
Estimation for Stochastic Damping Hamiltonian Systems under Partial Observation. I. Invariant density.
Stochastic Processes and their Applications Volume 124, Issue 3, pages 1236-1260 hal

Janon, A., Klein T., Lagnoux A., Nodet M., Prieur C. (2014),
Asymptotic normality and efficiency of two Sobol index estimators.
hal-00665048. ESAIM P&S, Volume 18, pages 342-364 hal

A. Janon, M. Nodet, C. Prieur (2014),
Uncertainties assessment in global sensitivity indices estimation from metamodels.
International Journal for Uncertainty Quantification 4, no. 1, pages 21-36 arxiv

A. Janon, M. Nodet, C. Prieur (2013),
Certified reduced-basis solutions of viscous Burgers equation parametrized by initial and boundary values.
ESAIM Math. Model. Numer. Anal. Volume 47, Issue 2, pages 317-348 arxiv

E. Di Bernardino, V. Maume-Deschamps, C. Prieur (2013),
Estimating Bivariate Tail: a copula based approach.
hal-00475386v2. Journal of Multivariate Analysis. Volume 119, pages 81-100 hal

E. Di Bernardino, T. Laloë, V. Maume-Deschamps & C. Prieur (2013),
Plug-in estimation of level sets in a non-compact setting with applications in multivariate risk theory.
hal-00580624v2. ESAIM Probab. Stat. Volume 17, pages 236-256.

G. Chastaing, F. Gamboa, C. Prieur (2012),
Generalized Hoeffding-Sobol Decomposition for Dependent Variables. Application to Sensitivity Analysis.
Electronic Journal of Statistics. Volume 6, Issue 0, pages 2420-2448 lien

J. Y. Tissot, C. Prieur (2012),
Bias correction for the estimation of sensitivity indices based on random balance designs.
hal-00507526v1. Reliability Engineering & System Safety. Volume 107, pages 205-213 hal

P. Cénac, V. Maume-Deschamps, C. Prieur (2012),
Some multivariate risk indicators; minimization by using a Kiefer-Wolfowitz approach to the mirror stochastic algorithm.
Statistics and Risk Modeling. Volume 29, Issue 1, pages 47-71 hal

A. Antoniadis, C. Helbert, C. Prieur & L. Viry (2012),
Spatio-temporal metamodeling for West African monsoon.
Environmetrics, Special Issue: Spatio-Temporal Stochastic Modelling. (METMAV)
Volume 23, Issue 1, pages 24-36 pdf

F. Gamboa, T. Klein, C. Prieur (2012),
Conditional large and moderate deviations for sums of discrete random variables. Combinatoric applications.
Bernoulli 18, 4, 1341-1360 pdf

O. Mestre, C. Gruber, C. Prieur, H. Caussinus & S. Jourdain (2011),
SPLIDHOM~: a method for homogenization of daily temperature observations.
Journal of Applied Meteorology and Climatology 50, 11, 2343-2358 pdf

N. Garcia, A. Galves, C. Prieur (2010),
Perfect simulation of the $\bar{d}$-minimal coupling between ordered pairs of binary chains of infinite order.
Journal of Statistical Physics 141, 669-682 pdf

N. Guillotin-Plantard, C. Prieur (2010),
Limit theorem for random walk in weakly dependent random scenery.
Annales de l'Institut Henri Poincaré P&S 46, 4, 1178-1194 pdf

N. Guillotin-Plantard and C. Prieur (2010),
Central limit theorem for sampled sums of dependent random variables.
ESAIM P&S. 14, 299-314 arxiv

J. Dedecker and C. Prieur (2009),
Some unbounded functions of intermittent maps for which the central limit theorem holds.
ALEA 5, 29-45 arxiv

B. Laurent, C. Ludena, C. Prieur (2008),
Adaptive estimation of linear functionals by model selection.
Electronic Journal of Statistics Vol. 2, 993-1020 lien

C. Prieur (2007),
Change Point Estimation by Local Linear Smoothing under a Weak Dependence Condition.
Math. Methods of Statist. Vol. 16 No. 1, 25-41 pdf

J. Dedecker & C. Prieur (2007),
An empirical central limit theorem for dependent sequences.
Stochastic Process. Appl. Vol. 117 No. 1, 121-142 pdf

B. Bercu & C. Prieur (2006),
Spectral properties of chaotic processes.
Stochastics and Dynamics. Vol. 6 No. 3, 355-371 pdf

A. L. Fougères, F. Gamboa & C. Prieur (2005),
Some asymptotic results for the number of generalized records,
Extremes, Vol. 8, 27-41 pdf

J. Dedecker & C. Prieur (2005),
New dependence coefficients. Examples and applications to statistics.
Probab. Theory and Relat. Fields, Vol. 132, 203-236 pdf
online paper

J. Dedecker & C. Prieur (2004),
Couplage pour la distance minimale.
C. R. Acad. Sci. Paris, Série 1, 338, 805-808 pdf

J. Dedecker & C. Prieur (2004),
Coupling for $tau$-dependent sequences and applications.
Journal of Theoretical Probability, Vol. 17 No. 4, 861-885 pdf

C. Prieur (2002),
An Empirical Functional Central Limit Theorem For Weakly Dependent Sequences.
Probab. and Math. Statistics, Vol. 22 Fasc. 2 , 259-287 ps

C. Prieur (2001),
Estimation de la densité invariante de systèmes dynamiques en dimension 1.
C.R. Acad. Sci. Paris ,t. 332 Série 1, 761-764 pdf

C. Prieur (2001),
Density Estimation For One-Dimensional Dynamical Systems.
ESAIM , Probab. & Statist. 5, p. 51-76 gzip-ps

P. Ango Nze & C. Prieur (2001),
Estimation d'une rupture en dépendance faible.
C.R. Acad. Sci. Paris ,t. 335 Série 1, 267-270. Proofs: ps.file

C. Coulon-Prieur & Paul Doukhan (2000),
A triangular central limit theorem under a new weak dependence condition. Statistic& Probability Letters , 47, 61-68 pdf
erratum.pdf

Proceedings

M. Billaud-Friess, A. Macherey, A. Nouy, and C. Prieur (2018),
Stochastic methods for solving high-dimensional partial differential equations.
International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, p. 125-141. Springer, Cham. lien

E. Grenier, M. Hoffmann, T. Lelièvre, V. Louvet, C. Prieur, N. Rachdi and P. Vigneaux (2014),
Statistical inference for partial differential equations.
ESAIM: Proceedings and Surveys, Vol. 45, p. 178-188. Congrès SMAI 2013, Seignosse le Penon, France, 27-31 mai 2013. Jean-Stéphane Dhersin (Ed.). lien

A. Janon, M. Nodet, C. Prieur and C. Prieur (2014),
Global sensitivity analysis for the boundary control of an open channel.
IEEE Conf. on Dec. and Cont. (CDC'14), Los Angeles (CA), USA, 2014. lien

T. Cartailler, A. Gauss, A. Janon, H. Monod, C. Prieur and N. Saint-Geours (2014),
Sensitivity analysis and uncertainty quantification for environmental models.
ESAIM Proceedings, Vol. 44, Journées MAS 2012.
hal-00789826 lien

A. Gauss, A. Bsaibes, T. Cartailler, C. Prieur, E. Lebon, F. Gerard, E. Jallas (2013),
A decision-support system for vineyard water status management based on a modeling approach.
Precision agriculture '13, Editor John V. Stafford. Wageningen Academic Publishers. 9th European Conference on Precise Agriculture (ECPA), Lleida, Catalonia (Spain), 2013. lien

A. Janon, M. Nodet, C. Prieur (2012),
Certified metamodels for sensitivity indices estimation.
Congrès National de Mathématiques Appliquées et Industrielles, ESAIM Proc. 35, 234-238, EDP Sci., Les Ulis, 2011.

C. Prieur (2002),
Density Estimation for One-Dimensional Dynamical Systems
Proceedings of the Seminar on Stability Problems for Stochastic Models, Part I (Eger, 2001)
Journal of Mathematical Sciences (New York), 111, 3, 3601-3612.

Technical reports

C. Blanchet-Scalliet et al.(2024),
Activity report ciroquo research & industry consortium hal

S. Anquetin et al. (2022),
Un Institut Environnement - Société à l'UGA Vers une pérennisation de la dynamique impulsée autour des études des trajectoires des socio-écosystèmes dans un contexte d'évolution climatique hal

H. Mohammadi, P. Challenor and C. Prieur
Variance-based global sensitivity analysis of numerical models using R arXiv

O. Roustant, R. Le Riche, R., J. Garnier et al. (2021),
Chair in applied mathematics OQUAIDO Activity report hal

A. Guaus, A. Bsaibes, T. Cartailler, C. Prieur, E. Lebond, F. Gérarda (2013),
Time-dependent sensitivity analysis of a model-driven decision support system for vineyard water status management.

J. Y. Tissot and C. Prieur (2012),
Variance-based sensitivity analysis using harmonic analysis.
hal-00680725 hal

Prepublications, submitted for publication

R. Verdière, C. Prieur and O. Zahm (2025),
Mollified Active Subspace.
Soumis pour publication, hal

C. Dutang and C. Prieur (2025), On the Use of Global Sensitivity Analysis in a Game-Theoretic Approach to an Environmental Management Problem.
Soumis pour publication.

D. Georges, M. Moro and C. Prieur (2025), PINN-Based Estimation of Distributed SIHRV Epidemiological Models.
Soumis pour publication.

P. Etoré, J. R. León and C. Prieur (2025),
About hypoelliptic SDE equations with boundary conditions.
Soumis pour publication, hal

C. Duhamel, C. Helbert, M. Munoz Zuniga, C. Prieur and D. Sinoquet (2025),
Active learning strategies for the estimation of a feasible set dened from a vector output black-box simulator.
Soumis pour publication, hal

A. Barry, F. Bachoc, S. Bouquet, M. Munoz Zuniga and C. Prieur (2024),
Optimal Design of Physical and Numerical Experiments for Computer Code Calibration
Soumis pour publication, hal

R. Wang, V. Maume-Deschamps and C. Prieur (2024),
Quantile oriented sensitivity analysis with random forest based on pinball loss
Soumis pour publication, hal

S. Da Veiga, F. Gamboa, T. Klein, A. Lagnoux and C. Prieur (2023),
Efficient estimation of Sobol’ indices of any order from a single input/output sample
Prépublication, hal

Other report

J.M. Bardet et C. Prieur (2003),
Propriétés rhéologiques de la pâte de ciment.
Rapport de contrat.