Dieses Bild zeigt Marco Oesting

Marco Oesting

Jun.-Prof. Dr. rer. nat.

SimTech-Tenure-Track-Professur, Leiter der Arbeitsgruppe für Computational Statistics
Stuttgarter Zentrum für Simulationswissenschaft (SC SimTech) & Institut für Stochastik und Anwendungen


Allmandring 5b
70569 Stuttgart
Raum: 1.33


Nach Vereinbarung via E-Mail


  • Extremwerttheorie und -statistik
  • räumliche Statistik
  • Simulation stochastischer Prozesse und Zufallsfelder
  • statistische Modellierung von Extremereignissen in Klima- und Umweltwissenschaften

Mehr Informationen zu Forschungsthemen und aktuellen Projekten finden Sie hier.


  • J. Lederer & M. Oesting.
    Extremes in high dimensions: Methods and scalable algorithms.
    Available at arXiv.
  • C. Forster & M. Oesting.
    Non-stationary max-stable models with an application to heavy rainfall data.
    Available at arXiv.
  • M. Oesting & R. Huser.
    Patterns in Spatio-Temporal Extremes.
    Available at arXiv.
  • J. Legrand, P. Naveau & M. Oesting.
    Evaluation of binary classifiers for asymptotically dependent and independent extremes.
    Available at arXiv.
  • M. Oesting & P. Naveau.
    Spatial Modeling of Heavy Precipitation by Coupling Weather Station Recordings and Ensemble Forecasts with Max-Stable Processes.
    Available at arXiv.
  • C. Dombry, S. Engelke & M. Oesting.
    Asymptotic Properties of the Maximum Likelihood Estimator for Multivariate Extreme Value Distributions.
    Available at arXiv.

Articles in Refereed Journals

  • M. Oesting & O. Wintenberger (2024+).
    Estimation of the Spectral Measure from Convex Combinations of Regularly Varying Random Vectors.
    Accepted for publication in the Annals of Statistics.
    Accepted version available at e-publications.org.
  • C. Bernard, A. Müller & M. Oesting (2024).
    Lp-norm spherical copulas.
    Journal of Multivariate Analysis 201, 105262.
    Available at ScienceDirect.
  • S. Fischer, M. Oesting & A. Schnurr (2024).
    Multivariate motion patterns and applications to rainfall radar data.
    Stochastic Environmental Research and Risk Assessment 38, 1235–1249.
    Available at link.springer.com.
  • M. Oesting, A. Rapp & E. Spodarev (2023).
    Detection of Long Range Dependence in the Time Domain for (In) Finite-Variance Time Series,
    Statistics 57(6), 1352–1379.
    Available at tandfonline.com.
  • M. Oesting & A. Rapp (2023).
    Long Memory of Max-Stable Time Series as Phase Transition: Asymptotic Behaviour of Tail Dependence Estimators,
    Electronic Journal of Statistics 17(2), 3316-3336.
    Available at projecteuclid.org.
  • O.E. Jurado, M. Oesting, H.W. Rust (2023).
    Implications of modeling seasonal differences in the extremal dependence of rainfall maxima.
    Stochastic Environmental Research and Risk Assessment 37, 1963-1981.
    Available at link.springer.com.
  • V. Wagner, B. Castellaz, M. Oesting & N. Radde (2022).
    Quasi-Entropy Closure: A Fast and Reliable Approach to Close the Moment Equations of the Chemical Master Equation.
    Bioinformatics 38(18), 4352–4359.
    Available at academic.oup.com.
  • M. Oesting & K. Strokorb (2022).
    A comparative tour through the simulation algorithms for max-stable processes.
    Statistical Science 37(1), 42-63.
    Available at projecteuclid.org.
  • V. Makogin, M. Oesting, A. Rapp & E. Spodarev (2021).
    Long Range Dependence for Stable Random Processes.
    Journal of Time Series Analysis 42(2), 161-185.
    Available onlinelibrary.wiley.com.
  • M. Oesting & A. Schnurr (2020).
    Ordinal Patterns in Clusters of Subsequent Extremes of Regularly Varying Time Series.
    Extremes 23, 521-545.
    Available at link.springer.com.
  • M. Oesting, M. Schlather & C. Schillings (2019).
    Sampling Sup-Normalized Spectral Functions for Brown-Resnick Processes.
    Stat 8(1), e228.
    Available at onlinelibrary.wiley.com.
  • S. Engelke, R. de Fondeville & M. Oesting (2019).
    Extremal Behavior of Aggregated Data with an Application to Downscaling.
    Biometrika 106(1), 127-144.
    Available at academic.oup.com
  • M. Oesting & K. Strokorb (2018).
    Efficient simulation of Brown-Resnick processes based on variance reduction of Gaussian processes.
    Advances in Applied Probability 50(4), 1155-1175.
    Available at cambridge.org.
  • M. Oesting, L. Bel & C. Lantuéjoul (2018).
    Sampling from a Max-Stable Process Conditional on a Homogeneous Functional with an Application for Downscaling Climate Data.
    Scandinavian Journal of Statistics 45(2), 382-404.
    Available at onlinelibrary.wiley.com.
  • M. Oesting (2018).
    Equivalent Representations of Max-Stable Processes via lp Norms.
    Journal of Applied Probability 55(1), 54-68.
    Available at cambridge.org.
  • M. Oesting & A. Stein (2018).
    Spatial Modeling of Drought Events Using Max-Stable Processes.
    Stochastic Environmental Research and Risk Assessment 32(1), 63-81.
    Available at link.springer.com.
  • M. Oesting, M. Schlather & C. Zhou (2018).
    Exact and Fast Simulation of Max-Stable Processes on a Compact Set Using the Normalized Spectral Representation.
    Bernoulli 24(2), 1497-1530.
    Available at projecteuclid.org.
  • C. Dombry, S. Engelke & M. Oesting (2017).
    Bayesian Inference for Multivariate Extreme Value Distributions.
    Electronic Journal of Statistics 11(2), 4813-4844.
    Available at projecteuclid.org.
  • M. Oesting, M. Schlather & P. Friederichs (2017).
    Statistical Post-Processing of Forecasts for Extremes Using Bivariate Brown-Resnick Processes with an Application to Wind Gusts.
    Extremes 20(2), 309-332.
    Available at link.springer.com.
  • C. Dombry, S. Engelke & M. Oesting (2016).
    Exact simulation of max-stable processes.
    Biometrika 103(2), 303-317.
    Available at oxfordjournals.org.
  • M. Schlather, A. Malinowski, P.J. Menck, M. Oesting & K. Strokorb (2015).
    Analysis, simulation and prediction of multivariate random fields with package RandomFields.
    Journal of Statistical Software 63(8), 1-25.
    Available at jstatsoft.org.
  • M. Oesting (2015).
    On the distribution of a max-stable process conditional on max-linear functionals.
    Statistics & Probability Letters 100, 158-163.
    Available at ScienceDirect.
  • S. Engelke, A. Malinowski, M. Oesting & M. Schlather (2014).
    Statistical inference for max-stable processes by conditioning on extreme events.
    Advances in Applied Probability 46(2), 478-495.
    Available at projecteuclid.org.
  • M. Oesting & M. Schlather (2014).
    Conditional Sampling for Max-Stable Processes with a Mixed Moving Maxima Representation.
    Extremes 17(1), 157-192.
    Available at link.springer.com.
  • M. Oesting, Z. Kabluchko & M. Schlather (2012).
    Simulation of Brown-Resnick processes.
    Extremes 15(1), 89-107.
    Available at link.springer.com.

 Book Chapters

  • C. Dombry, M. Oesting & M. Ribatet (2016).
    Conditional Simulation of Max-Stable Processes.
    In Dey, D.K., Yan, J. (Ed.), Extreme Value Modeling and Risk Analysis: Methods and Applications (pp. 215-238), Boca Raton: CRC Press.
  • M. Oesting, M. Ribatet & C. Dombry (2016).
    Simulation of Max-Stable Processes.
    In Dey, D.K., Yan, J. (Ed.), Extreme Value Modeling and Risk Analysis: Methods and Applications (pp. 195-214), Boca Raton: CRC Press.
  • M. Ribatet, C. Dombry & M. Oesting (2016).
    Spatial Extremes and Max-Stable Processes.
    In Dey, D.K., Yan, J. (Ed.), Extreme Value Modeling and Risk Analysis: Methods and Applications (pp. 179-194), Boca Raton: CRC Press.

Book Reviews

  • M. Oesting (2011).
    Book Review: Computational Statistics: An Introduction to R. Sawitzki (2009).
    Biometrical Journal, 53, 868.


  • M. Schlather, A. Malinowski, M. Oesting, D. Boecker, K. Strokorb, S. Engelke, J. Martini, F. Ballani, O. Moreva, J. Aue, P.J. Menck, S. Groß, U. Ober, P. Ribeiro, R. Singleton, B. Pfaff and R Core Team (2019).
    RandomFields: Simulation and Analysis of Random Fields.
    R package version 3.3.1. Available at CRAN.


  • M. Oesting (2020).
    Analysis and simulation of multivariate and spatial extremes.
    Habilitation thesis, Universität Siegen.
    Available at OPUS Siegen.
  • M. Oesting (2012).
    Spatial Interpolation and Prediction of Gaussian and Max-Stable Processes.
    PhD thesis, Georg-August-Universität Göttingen.
    Available at Niedersächsische Staats- und Universitätsbibliothek Göttingen.
  • M. Oesting (2009).
    Simulationsverfahren für Brown-Resnick-Prozesse.
    Diploma thesis, Georg-August-Universität Göttingen.
    Available at arXiv.

Summer Term 2024
Statistik für Wirtschaftswissenschaftler

Wintersemester 2023/2024
Stochastic Simulation I (Mathematik M.Sc. & SimTech M.Sc.)
Stochastik und Angewandte Mathematik für das Lehramt (Lehramt Mathematik)

Sommersemester 2023
Stochastische Prozesse (Mathematik B.Sc.)

Wintersemester 2022/23
Mathematische Statistik (Mathematik B.Sc.)

Sommersemester 2022
Maß- und Wahrscheinlichkeitstheorie (Mathematik B.Sc.)

Wintersemester 2021/22
Stochastic Simulation I (Mathematik M.Sc. & SimTech M.Sc.)

Sommersemester 2021
Stochastic Simulation II (Mathematik M.Sc. & SimTech M.Sc.)

Wintersemester 2020/21
Lineare Strukturen (SimTech B.Sc.)
Stochastic Simulation I (Mathematik M.Sc. & SimTech M.Sc.)

Curriculum Vitae

10/2005 - 09/2009 

Studies in Mathematics, University of Göttingen


Diploma in Mathematics with Prof. Dr. M. Schlather, University of Göttingen

10/2009 - 05/2012 

PhD student at the Institute for Mathematical Stochastics, University of Göttingen, within the DFG Research Training
Group 1023 "Identification in Mathematical Models: Synergy of Stochastic and Numerical Methods"


PhD in Mathematics with Prof. Dr. M. Schlather, University of Göttingen

06/2012 - 12/2013 

Research Assistant at the Institute of Mathematics, University of Mannheim, within the project WEX-MOP
(Mesoscale Weather Extremes: Theory, Spatial Modeling and Prediction; Volkswagen Stiftung)

12/2013 - 12/2014 


Postdoctoral Researcher at the Division of Applied Mathematics and Informatics (MIA), INRA/AgroParisTech, within
the project McSim (Multisupport conditional simulation of max-stable processes. Applications to the local prediction
of extreme climatic events; Agence Nationale de la Recherche)

01/2015 - 09/2015 

Postdoctoral Researcher at the Department of Earth Observation Science, Faculty of Geo-Information Science and
Earth Observation (ITC), University of Twente

10/2015 - 03/2018 and 10/2018 - 07/2020

Akademischer Rat auf Zeit at the Department of Mathematics, University of Siegen

04/2018 - 09/2018

Interim Professorship of Stochastics at the Faculty of Mathematics and Economics, University of Ulm


Habilitation in Mathematics; Department of Mathematics, University of Siegen

since 08/2020

Tenure-Track Professorship for Computational Statistics at the Stuttgart Center for Simulation Science (SC SimTech) and the Institute for Stochastics and Applications, University of Stuttgart

Ich bin Ansprechpartner für die Vortragsreihe "Mathe Macht! Mathematik in der Praxis", in der sich Unternehmen vorstellen, die Mathematik oder mathematische Methoden in unterschiedlichen Bereichen anwenden. Nährere Informationen finden Sie unter https://www.f08.uni-stuttgart.de/mathematik/studierende/mathemacht/

Von 2020 bis 2023 war ich Mitglied des Organisationsteams des 'One World Extremes Seminar'. Nährere Informationen finden Sie unter https://sites.google.com/view/ow-extremes/home

Herr Jun.-Prof. Oesting, was haben Hitzewellen, Unwetter und Hochwasser mit Mathematik zu tun?

zum Interview

Zum Seitenanfang