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).
  L_p-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 l^p 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.

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.