Preprints

1. Gnecco N., Peters J., Engelke S., Pfister N.
    Boosted control functions. [arxiv]
2. Engelke S., Ivanovs J. & Strokorb K.
    Graphical models for infinite measures with applications to extremes and Lévy processes. [arxiv]
3. Zhang Z., Bolin D., Engelke S., Huser R.
    Extremal Dependence of Moving Average Processes Driven by Exponential-Tailed Lévy Noise. [arxiv]
4. Hentschel M., Engelke S. & Segers J.
    Statistical inference for Hüsler-Reiss graphical models through matrix completions. [arxiv]
    Best Student Paper Award at the EVA conference 2023 for Manuel Hentschel
5. Pasche O. & Engelke S.
    Neural networks for extreme quantile regression with an application to forecasting of flood risk. [arxiv]
6. Engelke S., Lalancette M. & Volgushev S.
    Learning extremal graphical structures in high dimensions. [arxiv]
7. Birghila C., Aigner M. & Engelke S.
    Distributionally robust tail bounds based on Wasserstein distance and f-divergence. [arxiv]
8. Kimber T., Engelke S., Tetko I., Bruno E. & Godin G.
    Synergy effect between convolutional neural networks and the multiplicity of SMILES for improvement of molecular prediction. [arxiv]
9. Dombry C., Engelke S. & Oesting M.
     Asymptotic properties of likelihood estimators for multivariate extreme value distributions. [arxiv]

Peer-reviewed

1. Gnecco, N., Terefe, E.D., & Engelke S. (2023)
     Extremal random forests.
     Journal of the American Statistical Association, Theory and Methods, to appear. [arxiv]
2. Deuber D., Li J., Engelke S., & Maathuis M. (2023)
     Estimation and inference of extremal quantile treatment effects for heavy-tailed distributions.
     Journal of the American Statistical Association, Theory and Methods, to appear. [arxiv]
3. Deidda C., Engelke S., & De Michele, C. (2023)
     Asymmetric dependence in hydrological extremes.
     Water Resources Research,, to appear. [arxiv]
4. Zeder J., Sippel S., Pasche O., Engelke S., & E. Fischer (2023)
     The effect of a short observational record on the statistics of temperature extremes.
     Geophysical Research Letters, 50, e2023GL10409.
5. Röttger F., Engelke S. & Zwiernik P. (2023)
     Total positivity in multivariate extremes.
     Annals of Statistics, 51, 962-1004. [arxiv]
6. Velthoen J., Dombry C., Cai J.-J., & Engelke S. (2023)
     Gradient boosting for extreme quantile regression.
     Extremes, 26, 639-667. [arxiv]
7. Dupuis D., Engelke S. & Trapin L. (2023)
     Modeling panels of extremes.
     Annals of Applied Statistics, 17, 498-517. [arxiv]
8. Engelke S. & Volgushev S. (2022)
     Structure learning for extremal tree models.
     Journal of the Royal Statistical Society: Series B, 84, 2055-2087. [arxiv]
9. Bai Y., Lam H. & Engelke S. (2022)
    Rare-event simulation without variance reduction: an extreme value theory approach.
     IEEE 2022 Winter Simulation Conference, 49, 133-144.
    WSC PhD Colloquium INFORMS I-SIM Award 2022
10. Boulaguiem Y., Zscheischler J., Vignotto E., van der Wiel K. & Engelke S. (2022)
      Modelling and simulating spatial extremes by combining extreme value theory with generative adversarial networks.
      Environmental Data Science, to appear. [arxiv]
11. Engelke S. & Ivanovs J. (2021)
      Sparse Structures for Multivariate Extremes.
      Annual Review of Statistics and Its Application, 8, 241-270. [arxiv, presentation]
12. Lalancette M., Engelke S. & Volgushev S. (2021)
      Rank-based estimation under asymptotic dependence and independence, with applications to spatial extremes.
      Annals of Statistics, 49, 2552-2576. [arxiv]
13. Gnecco N., Meinshausen N., Peters J. & Engelke S. (2021)
      Causal discovery in heavy-tailed models.
      Annals of Statistics, 49: 1755-1778. [arxiv]
14. Vignotto E., Engelke S. & Zscheischler J. (2021)
      Clustering bivariate dependencies of compound precipitation and wind extremes over Great Britain and Ireland.
      Weather and Climate Extremes, 32.
15. Zscheischler J., Naveau P., Martius O., Engelke S. & Raible, C. (2021)
      Evaluating the dependence structure of compound precipitation and wind speed extremes.
      Earth System Dynamics, 12, 1-16.
16. Vignotto E. & Engelke S. (2020)
      Extreme value theory for anomaly detection - the GPD classifier.
      Extremes, 23, 501-520. [arxiv]
17. Engelke S. & Hitz A.S. (2020)
      Graphical models for extremes (with discussion).
      Journal of the Royal Statistical Society: Series B, 82: 871-932. [RSS discussion]
     Discussion paper at the Royal Statistical Society
18. Engelke S., Opitz T. & Wadsworth J. (2019).
      Extremal dependence of random scale constructions.
      Extremes, 22: 623-666. [arxiv , shiny]
19. Engelke S., de Fondeville R. & Oesting M. (2019).
      Extremal behavior of aggregated data with an application to downscaling.
      Biometrika, 106: 127-144. [arxiv]
20. Le P.D., Davison A.C., Engelke S., Leonard M. & Westra S. (2018).
      Dependence properties of spatial rainfall extremes and areal reduction factor. 
      Journal of Hydrology , 565: 711-719.
21. Asadi P., Engelke S. & Davison A.C. (2018).
      Optimal regionalization of extreme value distributions for flood estimation.
      Journal of Hydrology, 556: 182-193. [arxiv]
22. Dombry C., Engelke S. & Oesting M. (2017).
      Bayesian inference for multivariate extreme value distributions.
      Electronic Journal of Statistics, 11: 4813-4844. [arxiv]
23. Engelke S. & Ivanovs J. (2017).
      Robust bounds in multivariate extremes.
      Annals of Applied Probability, 27: 3706-3734. [arxiv]
     Best presentation CFENetwork - CMStatistics & CRoNoS Award
24. Dębicki, K., Engelke S. & Hashorva, E. (2017).
      Generalized Pickands constants and stationary max-stable processes.
      Extremes, 19: 1-6. [arxiv]
25. Dombry C., Engelke S. & Oesting M. (2016).
      Exact simulation of max-stable processes.
      Biometrika, 103: 303-317. [arxiv, code]
26. Engelke S. & Ivanovs J. (2016).
      A Lévy-derived process seen from its supremum and max-stable processes.
      Electronic Journal of Probability, 21: paper no. 14. [arxiv]
27. Engelke S. & Kabluchko Z. (2016).
      A characterization of the normal distribution using stationary max-stable processes.
      Extremes, 20: 493-517. [arxiv]
28. Asadi P., Davison A. C. & Engelke S. (2015).
      Extremes on river networks.
      Annals of Applied Statistics, 9: 2023-2050. [arxiv]
29. Engelke S., Malinowski A., Kabluchko Z. & Schlather M. (2015).
      Estimation of Hüsler-Reiss distributions and Brown-Resnick processes.
      Journal of the Royal Statistical Society: Series B, 77: 239-265.
30. Engelke S. & Kabluchko Z. (2015).
      Max-stable processes and stationary systems of Lévy particles.
      Stochastic Processes and their Applications, 125: 4272-4299. [arxiv]
31. Engelke S., Kabluchko Z. & Schlather M. (2015).
      Maxima of independent, non-identically distributed Gaussian vectors.
      Bernoulli, 21: 38-61. [arxiv]
32. Das B., Engelke S. & Hashorva E. (2015).
      Extremal behavior of squared Bessel processes attracted by the Brown-Resnick process.
      Stochastic Processes and their Applications, 125: 780-796. [arxiv]
33. Engelke S., Malinowski A., Oesting M. & Schlather M. (2014).
      Statistical inference for max-stable processes by conditioning on extreme events.
      Advances in Applied Probability, 46: 478-495. [arxiv]
34. Engelke S. & Woerner J.H.C. (2013).
      A unifying approach to fractional Lévy processes.
      Stochastics and Dynamics, 13: DOI: 10.1142/S021949371250017.
35. Engelke S., Kabluchko Z. & Schlather M. (2011).
      An equivalent representation of the Brown-Resnick process.
      Statistics & Probability Letters, 81: 1150-1154.
36. Engelke S. & Schlather M. (2011).
      Book review: Environmental and Ecological Statistics with S. Song S. Qian (2010). Boca Raton, FL, USA: Chapman & Hall/CRC.
      Biometrical Journal, 53: 867.

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