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[1]
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Gildas Mazo and Laurent Tournier.
An inference method for global sensitivity analysis.
working paper or preprint, 2024.
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[2]
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Henri Mermoz Kouye, Gildas Mazo, Clémentine Prieur, and Elisabeta Vergu.
Performing global sensitivity analysis on simulations of a
continuous-time Markov chain model motivated by epidemiology.
Computational and Applied Mathematics, 43(409), 2024.
Postprint available at https://hal.archives-ouvertes.fr/hal-03565729.
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[3]
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Michael Levine and Gildas Mazo.
A smoothed semiparametric likelihood for estimation of nonparametric
finite mixture models with a copula-based dependence structure.
Computational Statistics, 2024.
Accepted for publication. Postprint available at
https://hal.inrae.fr/hal-03900661.
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[4]
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Gildas Mazo, Dimitris Karlis, and Andrea Rau.
A randomized pairwise likelihood method for complex statistical
inferences.
Journal of the American Statistical Association, 0(ja):1--19,
2023.
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[5]
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Gildas Mazo.
A trade-off between explorations and repetitions for estimators of
two global sensitivity indices in stochastic models induced by probability
measures.
SIAM/ASA Journal on Uncertainty Quantification,
9(4):1673--1713, 2021.
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[6]
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S. Asenova, G. Mazo, and J. Segers.
Inference on extremal dependence in the domain of attraction of a
structured Hüsler-Reiss distribution motivated by a Markov tree with
latent variables.
Extremes, 24:461--500, 2021.
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[7]
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G. Mazo and F. Portier.
Parametric versus nonparametric: the fitness coefficient.
Scandinavian Journal of Statistics, 48:1344--1383, 2021.
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[8]
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G. Mazo and Y. Averyanov.
Constraining kernel estimators in semiparametric copula mixture
models.
Computational Statistics and Data Analysis, 138:170--189, 2019.
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[9]
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G. Mazo.
A semiparametric and location-shift copula-based mixture model.
Journal of Classification, 34(3):444--464, 2017.
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[10]
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F. Durante, S. Girard, and G. Mazo.
Marshall-olkin type copulas generated by a global shock.
Journal of Computational and Applied Mathematics, 296:638--648,
2016.
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[11]
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G. Mazo, S. Girard, and F. Forbes.
A flexible and tractable class of one-factor copulas.
Statistics and Computing, 26:965--979, 2016.
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[12]
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G. Mazo, S. Girard, and F. Forbes.
Weighted least square inference for multivariate copulas ba sed on
dependence coefficients.
ESAIM: Probability and Statistics, 19:746--765, 2015.
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[13]
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G. Mazo, S. Girard, and F. Forbes.
A class of multivariate copulas based on products of bivariate
copulas.
Journal of Multivariate Analysis, 140:363--376, 2015.
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[14]
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B. Barroca, P. Bernardara, S. Girard, and G. Mazo.
Can uncertainty in hazard evaluation be used as guidance for
implementation strategies in urban resilience?
Natural Hazards and Earth System Sciences, 15:25--34, 2015.
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[15]
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Fabrizio Durante, Stéphane Girard, and Gildas Mazo.
Copulas based on Marshall-Olkin machinery.
In Umberto Cherubini, Fabrizio Durante, and Sabrina Mulinacci,
editors, Marshall-Olkin Distributions---Advances in Theory and
Applications, pages 15--31, 2015.
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[16]
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C. Sabbah, G. Mazo, C. Paccard, F. Reyal, and P. Hupé.
Smethillium: spatial normalization method for illumina infinium
humanmethylation beadchip.
Bioinformatics, 27(12):1693--1695, 2011.
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