[1] | Michael Levine and Gildas Mazo. A smoothed semiparametric likelihood for estimation of nonparametric finite mixture models with a copula-based dependence structure. working paper or preprint, December 2022. [ bib | HAL | .pdf ] |
[2] | Henri Mermoz Kouye and Gildas Mazo. Asymptotic control of the mean-squared error for Monte Carlo sensitivity index estimators in stochastic models. working paper or preprint, October 2022. [ bib | HAL | .pdf ] |
[3] | Gildas Mazo, Dimitris Karlis, and Andrea Rau. A randomized pairwise likelihood method for complex statistical inferences. working paper or preprint, February 2022. [ bib | HAL | .pdf ] |
[4] | Henri Mermoz Kouye, Gildas Mazo, Clémentine Prieur, and Elisabeta Vergu. Exploiting deterministic algorithms to perform global sensitivity analysis for continuous-time Markov chain compartmental models with application to epidemiology. working paper or preprint, February 2022. [ bib | HAL | .pdf ] |
[5] | G. Mazo and F. Portier. Parametric versus nonparametric: the fitness coefficient. Scandinavian Journal of Statistics, 48:1344--1383, 2021. [ bib | DOI | HAL ] |
[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 | HAL ] |
[7] | 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 | HAL ] |
[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 | HAL ] |
[9] | G. Mazo. A semiparametric and location-shift copula-based mixture model. Journal of Classification, 34(3):444--464, 2017. [ bib | DOI | HAL ] |
[10] | 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 | HAL ] |
[11] | 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 | HAL ] |
[12] | 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 ] |
[13] | 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 | HAL ] |
[14] | 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 | HAL ] |
[15] | 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 | HAL ] |
[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 | HAL ] |
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