Heidi Seibold

About

II am a postdoc at the working group for Computational Statistics at LMU Munich. I work on reproducible and open research and machine learning methods for personalized medicine. I am a member of the LMU Open Science Center.

I did my PhD at the University of Zurich.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

Room 459

Phone: +49 89 2180 2925

Heidi.Seibold [at] stat.uni-muenchen.de

Research Interests

You Can Find me on

References

  1. Méndez Fernández D, Graziotin D, Wagner S, Seibold H (2019) Open Science in Software Engineering. arXiv e-prints, arXiv:1904.06499.
  2. Korepanova N, Seibold H, Steffen V, Hothorn T (2019) Survival Forests under Test: Impact of the Proportional Hazards Assumption on Prognostic and Predictive Forests for ALS Survival. Accepted: Statistical Methods in Medical Research. \urlhttps://arxiv.org/abs/1902.01587.
  3. Seibold H, Seibold H, Zeileis A, Hothorn T (2019) model4you: An R Package for Personalised Treatment Effect Estimation. Journal of Open Research Software. http://doi.org/10.5334/jors.219.
  4. Foster S, Mohler-Kuo M, Tay L, Hothorn T, Seibold H (2019) Estimating patient-specific treatment advantages in the ‘Treatment for Adolescents with Depression Study.’ Journal of Psychiatric Research 112, 61–70. https://doi.org/10.1016/j.jpsychires.2019.02.021.
  5. Seibold H, Hothorn T, Zeileis A (2018) Generalised linear model trees with global additive effects. Advances in Data Analysis and Classification. https://doi.org/10.1007/s11634-018-0342-1.
  6. Thomas M, Bornkamp B, Seibold H (2018) Subgroup identification in dose-finding trials via model-based recursive partitioning. Statistics in Medicine.
  7. Zhang Z, Seibold H, Vettore MV, Song W-J, François V (2018) Subgroup identification in clinical trials: an overview of available methods and their implementations with R. Annals of Translational Medicine 6, 122–122. https://doi.org/10.21037/atm.2018.03.07.
  8. Seibold H, Bernau C, Boulesteix A-L, Bin RD (2017) On the choice and influence of the number of boosting steps for high-dimensional linear Cox-models. Computational Statistics. https://doi.org/10.1007/s00180-017-0773-8.
  9. Casalicchio G, Bossek J, Lang M et al. (2017) OpenML: An R package to connect to the machine learning platform OpenML. Computational Statistics. https://doi.org/10.1007/s00180-017-0742-2.
  10. Seibold H, Zeileis A, Hothorn T (2017) Individual Treatment Effect Prediction for Amyotrophic Lateral Sclerosis Patients. Statistical Methods in Medical Research.
  11. Seibold H, Zeileis A, Hothorn T (2016) Model-Based Recursive Partitioning for Subgroup Analyses. The International Journal of Biostatistics 12, 45–63. https://doi.org/10.1515/ijb-2015-0032.
  12. Belotti E, Weder N, Bufka L et al. (2015) Patterns of lynx predation at the interface between protected areas and multi-use landscapes in central Europe. PloS one 10, e0138139. http://doi.org/10.1371/journal.pone.0138139.
  13. Ray R-R, Seibold H, Heurich M (2014) Invertebrates outcompete vertebrate facultative scavengers in simulated lynx kills in the Bavarian Forest National Park, Germany. 37, 77–88.