Institute of Mathematics > Staff > Dr. Annette Möller

Dr. Annette Möller

Clausthal University of Technology
Institute of Mathematics
Research group Applied Statistics
Room 319 / Erzstraße 1
38678 Clausthal-Zellerfeld

Phone: +49 5323 72-2879
Fax: +49 5323 72-2304
E-Mail: annette.moeller@tu-clausthal.de

Research Interests

  • Probabilistic Weather Forecasting
  • Spatial Statistics
  • Copulas
  • High-dimensional data
  • Functional data analysis
  • Statistical Learning
  • Time Series Analysis

Short CV

since 08/2016

Research Assistant, Institute of Applied Stochastics and Operations Research, Clausthal University of Technology

10/2013 - 08/2016

Research Assistant, Biometrics & Bioinformatics, University of Göttingen

07/2010 - 09/2013

Doctoral Student in the RTG 1653 Spatio/Temporal Graphical Models, University of Heidelberg

2010

Diploma in Statistics, Technical University of Dortmund

Scientific Network Ensemble Postprocessing

In the Scientific Network "Statistical Postprocessing of ensemble forecasts for various weather variables" fundet by the German Research Council (DFG)  a group of international researchers is working jointly on developing and implementing statistical methods for probabilistic forecasting of different weather variables

More information on the project and current activities can be found on the internal project page of the working group.

Publications

Published papers (peer-reviewed)

 

Book Chapters

 

Conference Proceedings with referee process

  • Möller, A. and Gertheiss, J. (2018). A classification tree for functional data. In Wood, S. (ed.): Proceedings of the 33rd International Workshop on Statistical Modelling, 219-224.
  • Möller, A. and Groß, J. (2017): A heteroscedastic probabilistic temperature
    forecasting model incorporating spread-error correlation and high-resolution forecasts, in Grzegorczyk, M. and Ceoldo, G. (eds.): Proceedings of the 32nd International Workshop on Statistical Modelling, 131-136.
  • Möller, A. and Groß, J. (2016): Probabilistic Temperature forecasting based on an AR model fitted to forecast errors, in Dupuy, J.-F. and Josse, J. (eds.): Proceedings of the 31th International Workshop on Statistical Modelling, 225-230.
  • Möller, A. (2015): Spatially adaptive probabilistic temperature forecasting using Markovian EMOS, in Friedl, H. and Wagner, H. (eds.): Proceedings of the 30th International Workshop on Statistical Modelling Volume II, 175-178.
  • Möller, A., Tutz, G. and Gertheiss, J. (2014): Random Forests for Functional Covariates, in T. Kneib, F. Sobotka, J. Fahrenholz, and H. Irmer (eds.): Proceedings of the 29th International Workshop on Statistical Modelling, 219-223.

 

Preprints

  • Möller, A., Spazzini, L., Kraus, D., Nagler, T. and Czado, C. (2018): Vine copula based postprocessing of ensemble forecasts for temperature.
  • Möller, A. and Gertheiss, J. (2018): A classification tree for functional data.
  • Möller, A., Groß, J. (2018): Probabilistic temperature forecasting with a heteroscedastic ensemble postprocessing model.
  • Möller, A., Gertheiss, J. and Hessel, E.F. (2016): Clustering pigs according to their RFID registrations: A functional data approach.
  • Möller, A., Thorarinsdottir, T.L., Lenkoski, A., and Gneiting T. (2016): Spatially adaptive, Bayesian estimation for probabilistic temperature forecasts. arXiv:1507.05066.

 

Software

  • Groß, J. and Möller, A. (2018): ensAR: Autoregressive postprocessing methods for ensemble forecasts. R package version 0.0.0.9000, 2016. URL github.com/JuGross/ensAR

 

 

Teaching

Self dependend teaching, TU Clausthal

  • Statistical Methods of Machine Learning (Summer term 2017, Summer term 2018, Winter term 2018/2019)
  • Preparatory Course in Mathematics (Winter term, 2017/2018, Summer term 2018)
  • Introduction to Probability Theorie and Statistics (Winter term 2018/2019)
  • Statistics I for engineering sciences (Winter term 2018/2019)

 

Teaching joint with Jan Gertheiss, TU Clausthal

  • Statistics I for engineering sciences (Winter term 2016/2017)

 

 

         

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