Gilles Stupfler

Professor of Statistics
University of Angers
Laboratoire Angevin de REcherche en MAthématiques (LAREMA)
2 Boulevard de Lavoisier, 49045 Angers Cedex, France

ORCID: 0000-0003-2497-9412

Contact information:
Office: Room I115
Email: gilles DOT stupfler AT univ DASH angers DOT fr
Twitter: AT GillesStupfler

I was one of the founders and original organisers of the One World Extremes seminar. If you are interested in receiving reminders of upcoming talks, please directly contact the current organizers.

I am the creator and current editor of the Extreme Value Analysis newsletter, written for the worldwide academic extreme value community, with regular information about conferences, workshops, events as well as offers for PhD and post-doctoral fellowships in the area of probabilistic and statistical extreme value analysis. If you wish to subscribe, feel free to send me an email.

You will find below selected information about my research activities. For more information, including about my teaching activities and community service, please see my full academic CV.

Research interests

My main area of research is extreme value analysis. Much of my recent work in this direction has focused on how to measure and estimate extreme risk, particularly in actuarial and financial contexts.

My work generally sits at the interface of extreme value theory and various subfields of statistics, such as: I defended my Habilitation on 3rd September 2020 at ENSAI. My Habilitation manuscript can be found here (on the TEL server). My PhD manuscript is also available on the TEL server here.

Publication list (most recent first)

    Statistics: Theory and Methods

  1. Daouia, A., Stupfler, G., Usseglio-Carleve, A. (2024). An expectile computation cookbook, Statistics and Computing 34(3): 103.
  2. Daouia, A., Padoan, S.A., Stupfler, G. (2024). Optimal weighted pooling for inference about the tail index and extreme quantiles, Bernoulli 30(2): 1287-1312.
  3. Daouia, A., Stupfler, G., Usseglio-Carleve, A. (2023). Bias-reduced and variance-corrected asymptotic Gaussian inference about extreme expectiles, Statistics and Computing, to appear.
  4. Daouia, A., Stupfler, G., Usseglio-Carleve, A. (2023). Inference for extremal regression with dependent heavy-tailed data, Annals of Statistics 51(5): 2040-2066.
  5. Mao, T., Stupfler, G., Yang, F. (2023). Asymptotic properties of generalized shortfall risk measures for heavy-tailed risks, Insurance: Mathematics and Economics 111: 173-192.
  6. Davison, A.C., Padoan, S.A., Stupfler, G. (2023). Tail risk inference via expectiles in heavy-tailed time series, Journal of Business and Economic Statistics 41(3): 876-889.
  7. Stupfler, G., Usseglio-Carleve, A. (2023). Composite bias-reduced Lp-quantile-based estimators of extreme quantiles and expectiles, Canadian Journal of Statistics 51(2): 704-742. A corrected version of Proposition 1, which contained a typo in the expression of the off-diagonal covariance term, is Proposition 1 here.
  8. Daouia, A., Gijbels, I., Stupfler, G. (2022). Extremile regression, Journal of the American Statistical Association 117(539): 1579-1586.
  9. Girard, S., Stupfler, G., Usseglio-Carleve, A. (2022). On automatic bias reduction for extreme expectile estimation, Statistics and Computing 32(4): 64.
  10. Kaibuchi, H., Kawasaki, Y., Stupfler, G. (2022). GARCH-UGH: A bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time series, Quantitative Finance 22(7): 1277-1294.
  11. Padoan, S.A., Stupfler, G. (2022). Joint inference on extreme expectiles for multivariate heavy-tailed distributions, Bernoulli 28(2): 1021-1048.
  12. Girard, S., Stupfler, G., Usseglio-Carleve, A. (2022). Nonparametric extreme conditional expectile estimation, Scandinavian Journal of Statistics 49(1): 78-115.
  13. Girard, S., Stupfler, G., Usseglio-Carleve, A. (2022). Functional estimation of extreme conditional expectiles, Econometrics and Statistics 21: 131-158.
  14. Girard, S., Stupfler, G., Usseglio-Carleve, A. (2021). Extreme conditional expectile estimation in heavy-tailed heteroscedastic regression models, Annals of Statistics 49(6): 3358-3382. Corrected versions of the proofs of Theorem 3.3 (ARMA model) and Theorem 3.4 (GARCH model), which originally used incorrect matrix representations of the time series involved, can be found here.
  15. Falk, M., Stupfler, G. (2021). The min-characteristic function: Characterizing distributions by their min-linear projections, Sankhya A 83(1): 254-282.
  16. Daouia, A., Girard, S., Stupfler, G. (2021). ExpectHill estimation, extreme risk and heavy tails, Journal of Econometrics 221(1): 97-117.
  17. Gardes, L., Girard, S., Stupfler, G. (2020). Beyond tail median and conditional tail expectation: Extreme risk estimation using tail Lp-optimization, Scandinavian Journal of Statistics 47(3): 922-949.
  18. Daouia, A., Girard, S., Stupfler, G. (2020). Tail expectile process and risk assessment, Bernoulli 26(1): 531-556.
  19. Stupfler, G. (2019). On a relationship between randomly and non-randomly thresholded empirical average excesses for heavy tails, Extremes 22(4): 749-769.
  20. 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.
  21. Falk, M., Stupfler, G. (2019). On a class of norms generated by nonnegative integrable distributions, Dependence Modeling 7(1): 259-278.
  22. Stupfler, G. (2019). On the study of extremes with dependent random right-censoring, Extremes 22(1): 97-129.
  23. Gardes, L., Stupfler, G. (2019). An integrated functional Weissman estimator for conditional extreme quantiles, REVSTAT: Statistical Journal 17(1): 109-144.
  24. Daouia, A., Girard, S., Stupfler, G. (2019). Extreme M-quantiles as risk measures: From L1 to Lp optimization, Bernoulli 25(1): 264-309. A version of the supplementary material containing corrections to certain proofs and the statement of Lemma 1 is available here. [With thanks to Antoine Usseglio-Carleve] A corrected version of Proposition 2 is Proposition 2 here.
  25. 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.
  26. 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.
  27. 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.
  28. Girard, S., Stupfler, G. (2017). Intriguing properties of extreme geometric quantiles, REVSTAT: Statistical Journal 15(1): 107-139.
  29. Falk, M., Stupfler, G. (2017). An offspring of multivariate extreme value theory: The max-characteristic function, Journal of Multivariate Analysis 154: 85-95.
  30. Stupfler, G. (2016). On the weak convergence of the kernel density estimator in the uniform topology, Electronic Communications in Probability 21: 1-13.
  31. Stupfler, G. (2016). Estimating the conditional extreme-value index under random right-censoring, Journal of Multivariate Analysis 144: 1-24.
  32. Girard, S., Stupfler, G. (2015). Extreme geometric quantiles in a multivariate regular variation framework, Extremes 18(4): 629-663.
  33. Meintanis, S.G., Stupfler, G. (2015). Transformations to symmetry based on the probability weighted characteristic function, Kybernetika 51(4): 571-587.
  34. 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.
  35. Gardes, L., Stupfler, G. (2015). Estimating extreme quantiles under random truncation, TEST 24(2): 207-227. An erratum is available here.
  36. Guillou, A., Loisel, S., Stupfler, G. (2015). Estimating the parameters of a seasonal Markov-modulated Poisson process, Statistical Methodology 26: 103-123.
  37. Stupfler, G. (2014). On the weak convergence of kernel density estimators in Lp spaces, Journal of Nonparametric Statistics 26(4): 721-735.
  38. Gardes, L., Stupfler, G. (2014). Estimation of the conditional tail index using a smoothed local Hill estimator, Extremes 17(1): 45-75.
  39. 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.
  40. Stupfler, G. (2013). A moment estimator for the conditional extreme-value index, Electronic Journal of Statistics 7: 2298-2343.
  41. 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.
  42. Girard, S., Guillou, A., Stupfler, G. (2013). Frontier estimation with kernel regression on high order moments, Journal of Multivariate Analysis 116: 172-189.
  43. 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.
  44. Girard, S., Guillou, A., Stupfler, G. (2012). Estimating an endpoint with high-order moments, TEST 21(4): 697-729.
  45. Applied statistics

  46. Bozhidarova, M., Ball, F., van Gennip, Y., O'Dea, R.D., Stupfler, G. (2024). Describing financial crisis propagation through epidemic modelling on multiplex networks, Proceedings of the Royal Society A 480(2287): 20230787.
  47. Daouia, A., Stupfler, G., Usseglio-Carleve, A. (2023). Extreme value modelling of SARS-CoV-2 community transmission using discrete Generalised Pareto distributions, Royal Society Open Science 10(3): 220977.
  48. Thompson, A., Southon, N., Fern, F., Stupfler, G., Leach, R. (2021). Efficient empirical determination of maximum permissible error in coordinate metrology, Measurement Science and Technology 32: 105013.
  49. Church, O., Derclaye, E., Stupfler, G. (2021). Design litigation in the EU Member States: Are overlaps with other intellectual property rights and unfair competition problematic and are SMEs benefitting from the EU design legal framework?, European Law Review 46(1): 37-60. The published version (behind the Westlaw paywall) is available here.
  50. Mitchell, E.G., Crout, N.M.J., Wilson, P., Wood, A.T.A., Stupfler, G. (2020). Operating at the extreme: Estimating the upper yield boundary of winter wheat production in commercial practice, Royal Society Open Science 7(4): 191919.
  51. 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.
  52. 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.
  53. Book chapters

  54. Daouia, A., Stupfler, G. (2024). Extremile regression, in Wiley StatsRef: Statistics Reference Online.
  55. Girard, S., Stupfler, G., Usseglio-Carleve, A. (2021). Extreme Lp-quantile kernel regression, in Advances in Contemporary Statistics and Econometrics - Festschrift in Honor of Christine Thomas-Agnan (A. Daouia and A. Ruiz-Gazen, editors), pp. 197-219.

PhD supervision

Postdoctoral supervision