Recherche

STUPFLER Gilles

Enseignant-chercheur en statistique DOMAINES DE RECHERCHE
  • Valeurs extrêmes
  • Statistique semi- et non-paramétrique
  • M-estimation 
  • Contextes de données manquantes 
  • Modèles de Markov caché
Bureau 255 Téléphone +33 (0)2 99 05 32 55 Email gilles.stupfler@ensai.fr

Ma recherche se concentre principalement en théorie des valeurs extrêmes. Une grande part de mes travaux récents se consacre à la mesure et l'estimation du risque, en particulier dans des contextes financiers ou d'assurance. 

Je m'intéresse aussi aux questions non-paramétriques (en particulier la régression) et à l'analyse d'extrêmes en présence de données manquantes.

Mon CV complet contient davantage d'informations sur mon parcours, ainsi que sur mes activités d'enseignement et recherche. 

(Plus récente en premier)

  1. Daouia, A., Gijbels, I., Stupfler, G. (2019). Extremiles: A new perspective on asymmetric least squares, Journal of the American Statistical Association 114(527): 1366-1381.
  2. Falk, M., Stupfler, G. (2019). The min-characteristic function: characterizing distributions by their min-linear projections, Sankhya, to appear. 
  3. Gardes, L., Girard, S., Stupfler, G. (2019). Beyond tail median and conditional tail expectation: extreme risk estimation using tail Lp-optimisation, Scandinavian Journal of Statistics, to appear.
  4. Falk, M., Stupfler, G. (2019). On a class of norms generated by nonnegative integrable distributions, Dependence Modeling 7(1): 259-278.
  5. Daouia, A., Girard, S., Stupfler, G. (2019). Tail expectile process and risk assessment, Bernoulli, to appear.
  6. Stupfler, G. (2019). On a relationship between randomly and non-randomly thresholded empirical average excesses for heavy tails, Extremes, to appear.
  7. Stupfler, G. (2019). On the study of extremes with dependent random right-censoring, Extremes 22(1): 97-129.
  8. Church, O., Derclaye, E., Stupfler, G. (2019). An empirical analysis of the design case law of the EU Member States, International Review of Intellectual Property and Competition Law 50(6): 685-719.
  9. Gardes, L., Stupfler, G. (2019). An integrated functional Weissman estimator for conditional extreme quantiles, REVSTAT: Statistical Journal 17(1): 109-144.
  10. Daouia, A., Girard, S., Stupfler, G. (2019). Extreme M-quantiles as risk measures: From L1 to Lp optimization, Bernoulli 25(1): 264-309. Une version du supplément avec des corrections techniques (n'affectant pas la validité des résultats principaux) est ici. [Merci à Antoine Usseglio-Carleve]
  11. El Methni, J., Stupfler, G. (2018). Improved estimators of extreme Wang distortion risk measures for very heavy-tailed distributions, Econometrics and Statistics 6: 129-148.
  12. Daouia, A., Girard, S., Stupfler, G. (2018). Estimation of tail risk based on extreme expectiles, Journal of the Royal Statistical Society: Series B 80(2): 263-292.
  13. Stupfler, G., Yang, F. (2018). Analyzing and predicting CAT bond premiums: a Financial Loss premium principle and extreme value modeling, ASTIN Bulletin 48(1): 375-411.
  14. El Methni, J., Stupfler, G. (2017). Extreme versions of Wang risk measures and their estimation for heavy-tailed distributions, Statistica Sinica 27(2): 907-930.
  15. Girard, S., Stupfler, G. (2017). Intriguing properties of extreme geometric quantiles, REVSTAT: Statistical Journal 15(1): 107-139.
  16. Falk, M., Stupfler, G. (2017). An offspring of multivariate extreme value theory: the max-characteristic function, Journal of Multivariate Analysis 154: 85-95.
  17. Stupfler, G. (2016). On the weak convergence of the kernel density estimator in the uniform topology, Electronic Communications in Probability 21(17): 1-13.
  18. Stupfler, G. (2016). Estimating the conditional extreme-value index under random right-censoring, Journal of Multivariate Analysis 144: 1-24.
  19. Girard, S., Stupfler, G. (2015). Extreme geometric quantiles in a multivariate regular variation framework, Extremes 18(4): 629-663.
  20. Meintanis, S.G., Stupfler, G. (2015). Transformations to symmetry based on the probability weighted characteristic function, Kybernetika 51(4): 571-587.
  21. Goegebeur, Y., Guillou, A., Stupfler, G. (2015). Uniform asymptotic properties of a nonparametric regression estimator of conditional tails, Annales de l'Institut Henri Poincaré (B): Probability and Statistics 51(3): 1190-1213.
  22. Gardes, L., Stupfler, G. (2015). Estimating extreme quantiles under random truncation, TEST 24(2): 207-227. An erratum, also published in TEST, is available here.
  23. Guillou, A., Loisel, S., Stupfler, G. (2015). Estimating the parameters of a seasonal Markov-modulated Poisson process, Statistical Methodology 26: 103-123.
  24. Stupfler, G. (2014). On the weak convergence of kernel density estimators in Lp spaces, Journal of Nonparametric Statistics 26(4): 721-735.
  25. Gardes, L., Stupfler, G. (2014). Estimation of the conditional tail index using a smoothed local Hill estimator, Extremes 17(1): 45-75.
  26. Girard, S., Guillou, A., Stupfler, G. (2014). Uniform strong consistency of a frontier estimator using kernel regression on high order moments, ESAIM: Probability and Statistics 18: 642-666.
  27. Stupfler, G. (2013). A moment estimator for the conditional extreme-value index, Electronic Journal of Statistics 7: 2298-2343.
  28. Guillou, A., Loisel, S., Stupfler, G. (2013). Estimation of the parameters of a Markov-modulated loss process in insurance, Insurance: Mathematics and Economics 53(2): 388-404.
  29. Girard, S., Guillou, A., Stupfler, G. (2013). Frontier estimation with kernel regression on high order moments, Journal of Multivariate Analysis 116: 172-189.
  30. Girard, S., Guillou, A., Stupfler, G. (2012). Estimating an endpoint with high order moments in the Weibull domain of attraction, Statistics and Probability Letters 82(12): 2136-2144.
  31. Girard, S., Guillou, A., Stupfler, G. (2012). Estimating an endpoint with high-order moments, TEST 21(4): 697-729.