My publications are on Google Scholar

  • Städler N., Dondelinger F., Hill S., Akbani R., Lu Y., Mills G., Mukherjee S. (2017). Molecular Heterogeneity at the Network Level: High-Dimensional Testing, Clustering and a TCGA Case Study. Bioinformatics.
  • Städler, N., Shang, A., Bosch, F., Briggs, A., Goede, V., Berthier, A., Renaudin, C. and Leblond, V. (2016). A Systematic Review and Network Meta-Analysis to Evaluate the Comparative Efficacy of Interventions for Unfit Patients with Chronic Lymphocytic Leukemia. Advances in Therapy.
  • Städler, N. and Mukherjee, S. (2016). Non-nested hypothesis testing with diverging parameter dimensions. Technical Report (pdf).
  • Städler, N. and Mukherjee, S. (2016). Two-sample testing in high dimensions. Journal of the Royal Statistical Society: Series B (Statistical Methodology). doi: 10.1111/rssb.12173. (preprint arXiv:1210.4584.)
  • Städler, N. and Dondelinger, F. (2014). nethet: A bioconductor package for high-dimensional exploration of biological network heterogeneity. R package version 1.0.0.
  • Städler, N. and Mukherjee, S. (2014). Multivariate gene-set testing based on graphical models. Biostatistics 16 (1), 47-59. (preprint arXiv:1308.2771.)
  • Städler, N., Stekhoven, D. and Bühlmann, P. (2014). Pattern Alternating Maximization Algorithm for Missing Data in High-Dimensional Problems. Journal of Machine Learning Research, 15, 1903−1928. Pdf.
  • Städler, N. and Mukherjee, S. (2013). Penalized estimation in high-dimensional hidden Markov models with state-specific graphical models. Annals of Applied Statistics, 7, 2157-2179. Published in at this http URL. Pdf.
  • Städler, N. and Bühlmann, P. (2010). Missing values: sparse inverse covariance estimation and an extension to sparse regression. Statistics and Computing, 2012, Volume 22, 219-235. arXiv:0903.5463.
  • Städler, N., Bühlmann, P. and van de Geer, S. (2010). l1-penalization for mixture regression models (with discussion). Test 19, 209-285. arXiv:1202.6046.
  • Huber, D., Städler, N., Jonasson, L., et al. (2007). Multi-sensor data fusion for non-invasive continuous glucose monitoring. The 10th International Conference on Information Fusion. Download paper.
  • Akbani, R., Ng, K.-S., Werner, H., Shahmoradgoli, M., Zhang, F., Ju, Z., Liu, W., Yang, J.-Y., Yoshihara, K., Li, J, Ling, S., Seviour, E., Ram, P., Minna, J., Diao, L., Tong, P., Heymach, J., Hill, S., Dondelinger, F., Städler, N., Byers, L., Meric-Bernstam, F., Weinstein, J., Broom, W., Verhaak, R., Liang, H., Mukherjee, S., Lu, J., Mills, G. (2014). A pan-cancer proteomic perspective on the Cancer Genome Atlas. To appear in Nature Communications

Ph.D. Thesis

EM-type algorithms for non-convex and high-dimensional problems (Adviser: Prof. Dr. Peter Bühlmann)

Master’s Thesis

Statistische Modellentwicklung für nichtinvasive Blutzuckermessung mittels Sensoren (Adviser: Prof. Dr. Werner Stahel)