This image shows Marco Oesting

Marco Oesting

Jun.-Prof. Dr. rer. nat.

SimTech-Tenure-Track Professorship, Head of the Research Group for Computational Statistics
Stuttgart Center for Simulation Science (SC SimTech) & Institute for Stochastics and Applications

Contact

+49 711 685 65392

Allmandring 5b
70569 Stuttgart
Germany
Room: 1.33

Office Hours

Please contact me by E-Mail

Subject

  • Extreme value theory and statistics
  • Spatial Statistics
  • Simulation of stochastic processes and random fields
  • Statistical modelling of extreme events in climate and environmental sciences

More information on research topics and current projects can be found here.

Preprints

  • 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.
    Preprint available at HAL.
  • 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.

Software

  • 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.

Theses

  • 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

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

Summer Term 2023
Stochastische Prozesse (Mathematik B.Sc.)

Winter Term 2022/23
Mathematische Statistik (Mathematik B.Sc.)

Summer Term 2022
Maß- und Wahrscheinlichkeitstheorie (Mathematik B.Sc.)

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

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

Winter Term 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

09/2009

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"

05/2012 

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

08/2020

 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

I am the contact person for the lecture series "Mathe Macht! Mathematik in der Praxis", where companies that use mathemaYou can find more information under https://www.f08.uni-stuttgart.de/mathematik/studierende/mathemacht/

From 2020 to 2023, I was a member of the organizing team of the 'One World Extremes Seminar'. You can find more information under https://sites.google.com/view/ow-extremes/home

Jun.-Prof. Oesting, is there a connection between heat waves, storms and floods and mathematics?

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