Institute of Stochastics

Dr. Marco Oesting

Teaching

Summer Term 2018

 

Email address

 marco.oesting(at)uni-ulm.de

Phone

 +49 (0)731/50-23533

Fax

+49 (0)731/50-23649

Mailing address
  • Room no. E00
    Helmholtzstr. 18
    D-89069 Ulm

Office hours

by appointment

Short CV

  • since 04/2018: Interim Professor of stochastics at the Faculty of Mathematics and Economics, University of Ulm
  • since 10/2015: Akademischer Rat auf Zeit at the Department of Mathematics, University of Siegen (currently on leave)
  • 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
  • 12/2013 - 12/2014: Postdoctoral Researcher at the Division of Applied Mathematics and Informatics (MIA), INRA/AgroParisTech, within the project McSim (Agence Nationale de la Recherche)
  • 06/2012 - 12/2013: Research Assistant at the Institute of Mathematics, University of Mannheim, within the project WEX-MOP (Volkswagen Stiftung)
  • 05/2012: PhD 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
  • 09/2009: Diploma in Mathematics with Prof. Dr. M. Schlather, University of Göttingen
  • 10/2005 - 09/2009: Studies in Mathematics, University of Göttingen

Preprints / Publications

Preprints

  • M. Oesting, K. Strokorb. Efficient simulation of Brown-Resnick processes based on variance reduction of Gaussian 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 Journals

  • S. Engelke, R. de Fondeville, M. Oesting. Extremal Behavior of Aggregated Data with an Application to Downscaling. To appear in Biometrika. Preprint available at arXiv. 
  • 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, 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.
  • M. Oesting (2011). Book Review: Computational Statistics: An Introduction to R. Sawitzki (2009). Biometrical Journal, 53, 868.

 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, C. Berreth, K. Burmeister, J. Manitz, P. Ribeiro, R. Singleton, B. Pfaff, R Core Team (2017). RandomFields: Simulation and Analysis of Random Fields. R package version 3.1.50. Available at CRAN.

Theses

  • 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.