Publications 2009

• Audrino, F. and Bühlmann, P. (2009). Splines for financial volatility. Journal of the Royal Statistical Society: Series B, 71, 655-670.
• Blum, A., Coja-Oghlan, A., Frieze, A. and Zhou, S. (2009). Separating Populations with Wide Data: a Spectral Analysis, Electronic Journal of Statistics, Volume 3, Pages: 76-113.
• Buller, F., Zhang, Y., Scheuermann, J., Schäfer, J., Bühlmann, P. and Neri, D. (2009). Discovery of TNF inhibitors from a DNA-encoded chemical library based on Diels-Alder cycloaddition. Chemistry & Biology 16, 1075-1086.
• Buser, C.M., Künsch, H.R., Lüthi, D., Wild, M. and Schär, C.. (2009).Bayesian multi-model projection of climate: bias assumptions and interannual variability. Climate Dynamics 33), 849-868.
• Jenkner, J., Sprenger, M., Schwenk, I., Schwierz, C., Dierer, S. and Leuenberger, D. (2009). Detection and climatology of fronts in a high-resolution model reanalysis over the Alps, Meteorological Applications, available online, doi:10.1002/met.142
• Lounici, K., Pontil, M., Tsybakov, A.B. and van de Geer, S.A. (2009). Taking advantage of sparsity in multi-task learning. COLT 2009.
• Maathuis, M.H., Kalisch, M. and Bühlmann, P. (2009). Estimating high-dimensional intervention effects from observational data. Annals of Statistics 37, 3133-3164.
  
• Meier, L., van de Geer, S. and Bühlmann, P. (2009). High-dimensional additive modeling. Annals of Statistics 37, 3779-3821.
• Meinshausen, N., Meier, L. and Bühlmann, P. (2009). p-values for high-dimensional regression. Journal of the American Statistical Association 104, 1671-1681.
• Mitchell, C. and van de Geer, S. (2009). General oracle inequalities for model selection. Electronic Journal of Statistics 3, 176-204
• Rütimann, P. and Bühlmann, P. (2009). High dimensional sparse covariance estimation via directed acyclic graphs. Electronic Journal of Statistics 3, 1133-1160.
• Schwierz, C., Köllner-Heck, P., Zenklusen Mutter, E., Bresch, D.N., Vidale, P.-L., Wild, M. and Schär, C. (2009). Modelling European winter wind storm losses in current and future climate, Climatic Change, available online, doi:10.1007/s10584-009-9712-1
• Sodemann, H., Wernli, H. and Schwierz, C. (2009). Sources of water vapour contributing to the Elbe flood in August 2002 A tagging study in a mesoscale model, Q. J. R. Met. Soc., 135 (638), pp. 205 - 223, doi:10.1002/qj.374
• Tomassini, L., Reichert, P., Künsch, H.R., Buser, C.M., Knutti, R. and Borsuk, M.E.. (2009). A smoothing algorithm for estimating stochastic, continuous-time model parameters and its application to a simple climate model. Applied Statistics 58, 679-704.
• van de Geer, S. and Bühlmann, P. (2009). On the conditions used to prove oracle results for the Lasso. Electronic Journal of Statistics 3, 1360-1392.
• Zhou, S. (2009). Thresholding Procedures for High Dimensional Variable Selection and Statistical Estimation, In 23rd Annual Conference on Neural Information Processing Systems, December 2009.
• Zhou, S., Lafferty, J. and Wasserman, L. (2009).Compressed and Privacy Sensitive Sparse Regression, IEEE Transactions on Information Theory, Vol.55, No.2, Pages: 846 -866.
• Zhou, S., Ligett, K. and Wasserman, L. (2009). Differential Privacy with Compression, In 2009 IEEE International Symposium on Information Theory. Seoul, Korea.

Preprints

• Schöner, D., Dahinden, C., Gruissem, W. and Bühlmann, P. (2009). Robust prediction of hubs in the yeast synthetic lethal network.
• Zhou, S. (2009). Restricted Eigenvalue Conditions on Subgaussian Random Matrices. Preprint arXiv: 0912.4045.
• Zhou, S., van de Geer, S. and Buhlmann, P. (2009). Adaptive Lasso for high dimensional regression and graphical modeling. Preprint available at arXiv: 0903.2515.

Contribution to books

• Künsch, H.R. (2009). Begriffe, Formeln, Tabellen, Mathematik { Physik { Chemie (mit W. Durandi, B. Dzung, M. Kriener, A. Vogelsanger, S. Byland, K. Koch, A. Bartlome, M. Bleichenbacher, H. Roth). Orell Füssli Verlag AG, Zürich.