Preprints

• C. Bernard, A. Müller & M. Oesting. L_p-norm spherical copulas. Available at arXiv.
• M. Oesting, A. Rapp & E. Spodarev. Detection of Long Range Dependence in the Time Domain for (In) Finite-Variance Time Series. Available at arXiv.
• M. Oesting & O. Wintenberger. Estimation of the Spectral Measure from Convex Combinations of Regularly Varying Random Vectors. 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

• 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. To appear in Bioinformatics. Available at bioRxiv.
• 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.