[1]	Gildas Mazo and Laurent Tournier. An inference method for global sensitivity analysis. working paper or preprint, 2024. [ bib \| http ]
[2]	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. [ bib \| DOI ]
[3]	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. [ bib ]
[4]	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. [ bib \| DOI \| http ]
[5]	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. [ bib \| DOI \| http ]
[6]	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. [ bib \| DOI \| http ]
[7]	G. Mazo and F. Portier. Parametric versus nonparametric: the fitness coefficient. Scandinavian Journal of Statistics, 48:1344--1383, 2021. [ bib \| DOI \| http ]
[8]	G. Mazo and Y. Averyanov. Constraining kernel estimators in semiparametric copula mixture models. Computational Statistics and Data Analysis, 138:170--189, 2019. [ bib \| DOI \| http ]
[9]	G. Mazo. A semiparametric and location-shift copula-based mixture model. Journal of Classification, 34(3):444--464, 2017. [ bib \| DOI \| http ]
[10]	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. [ bib \| DOI \| http ]
[11]	G. Mazo, S. Girard, and F. Forbes. A flexible and tractable class of one-factor copulas. Statistics and Computing, 26:965--979, 2016. [ bib \| DOI \| http ]
[12]	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. [ bib \| DOI \| http ]
[13]	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. [ bib \| DOI \| http ]
[14]	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. [ bib \| DOI \| http ]
[15]	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. [ bib ]
[16]	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. [ bib \| DOI \| http ]

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