- Bühlmann, P. and Mandozzi, J. (2014). High-dimensional variable
screening and bias in subsequent inference, with an empirical
comparison. Computational Statistics 29, 407-430.PDF
- Bühlmann, P., Meier, L. and van de Geer, S. (2014). Invited Discussion on
"A significance test for the Lasso (R. Lockhart, J. Taylor,
R. Tibshirani and R. Tibshirani)". Annals of
Statistics 42, 469-477.PDF
- Bühlmann, P. (2014). Discussion of Big Bayes stories and
BayesBag. Statistical Science 29, 91-94.PDF
- Bühlmann, P., Kalisch, M. and Meier, L. (2014). High-dimensional
statistics with a view towards applications in biology. Annual Review of
Statistics and its Applications 1, 255-278. Download
- Choy, T. and Meinshausen, N. (2014). Sparse distance metric learning Computational Statistics 29, 515-528
- Gerster, S., Kwon, T., Ludwig, C., Matondo, M., Vogel, C.,
Marcotte, E., Aebersold, R. and Bühlmann, P. (2014). Statistical
approach to protein quantification. Molecular and Cellular
Proteomics 13, 666-677. Download
- Hauser, A. and Bühlmann, P. (2014). Two optimal strategies for
active learning of causal models from interventional data. International
Journal of Approximate Reasoning 55, 926-939. (This is a longer
paper version of Hauser and Bühlmann (PGM 2012)).PDF
- Hauser, A. and Bühlmann, P. (2014). Jointly interventional and
observational data: estimation of interventional Markov equivalence
classes of directed acyclic graphs. Journal of the Royal
Statistical Society, Series B, Article first published online: 30 May
2014, DOI: 10.1111/rssb.12071. PDF
- Hothorn, T., Kneib, T. and Bühlmann, P. (2014). Conditional
transformation models. Journal of the Royal Statistical
Society, Series B, 76, 3-37. PDF
- Kalisch, M. and Bühlmann, P. (2014). Causal structure learning
and inference: a selective review. Quality Technology & Quantitative
Management 11, 3-21.
- Kerkhoff, C., Schär, C. and Künsch, H.R. (2014) Assessment of bias assumptions for climate models, J. Climate accepted.
- Lin, S., Uhler, C., Sturmfels, B. and Bühlmann,
P. (2014). Hypersurfaces and their singularities in partial correlation
testing. Foundations of Computational Mathematics, Article
first published online: May 2014, DOI 10.1007/s10208-014-9205-0. PDF
- Peters, J. and Bühlmann, P. (2014). Identifiability of Gaussian
structural equation models with equal error variances. Biometrika 101,
- Shah, R. and Meinshausen, N. (2014). Random Intersection Trees
Journal of Machine
Learning Research 15, 629-654
- van de Geer, S. (2014). On the uniform convergence of empirical norms and inner products, with application to causal inference. Electronic Journal of Statistics 8, 543-574.
- van de Geer, S., Buehlmann, P., Ritov, Y and Dezeure, R.
(2014). On asymptotically optimal confidence regions and tests for high-dimensional models. Annals of Statistics 42, 1166-1202
- van de Geer, S. (2014). Weakly decomposable regularization penalties and structured
sparsity. Scandinavian Journal of Statistics, 2014, Vol. 41, 72-86.
- Bühlmann, P. (2014). Invited Discussion on "The Evolution of Boosting
Algorithms" and "Extending Statistical Boosting" (A. Mayr, H. Binder,
O. Gefeller and M. Schmid). To appear in Methods of Information in
- Chichignoud, M. and van de Geer, S. (2014). High Dimensional statistics in
non-regular Models. work in progress.
- Chichignoud, M., Lederer, J., van de Geer, S. and Wainwright, M.(2014) Calibration of the Lasso for prediction and variable selection. work in progress.
- Chichignoud, M., Pham Ngoc, T.M. and Rivoirard, V. (2014). Adaptive wavelet
estimation in nonparametric regression with errors-in-variables. Submitted
- Chichignoud, M. and Loustau, S. (2014) Bandwidth Selection in kernel empirical
risk minimization. Submitted.
- Colombo, D. and Maathuis, M.H. (2014). Order-independent
constraint-based causal structure learning. Journal of Machine
Research, to appear. (arXiv:1211.3295v2)
- Ernest, J. and Bühlmann, P. (2014). On the role of additive regression for (high-dimensional) causal inference. Preprint arXiv:1405.1868
- Jankova, J. and van de Geer, S. (2014). Confidence intervals for high- dimensional inverse covariance estimation.
Preprint available at ArXiv
- McWilliams, B., Heinze, C., Meinshausen, N., Krummenacher, G. and Vanchinathan, H. (2014). LOCO: Distributing Ridge Regression with Random Projections (abstract at arxiv:stat/1406.3469)
- Meinshausen, N. and Bühlmann, P. (2014). Maximin effects in inhomogeneous large-scale data. Preprint arXiv:1406.0596
- Nandy, P., Maathuis, M.H. and Richardson, T.S. (2014). Estimating the effect
of joint interventions from observational data in sparse high-dimensional
- Sokol, A., Maathuis, M.H. and Falkeborg,B. (2014). Quantifying
identifiability in independent component analysis. Electronic
Journal of Statistics, to appear. (arXiv:1401.7899v1)
- Städler, N., Stekhoven, D.J. and Bühlmann,
P. (2014). Pattern alternating maximization algorithm for missing data in
large p, small n problems. To appear in the Journal of Machine Learning Research. Preprint arXiv:1005.036
- van de Geer, S. and Muro, A. (2014). The additive model with different smoothness for the components. Preprint available at ArXiv
- van de Geer, S. and Muro, A. (2014). On higher order isotropy conditions and lower bounds for sparse quadratic forms.
Preprint available at ArXiv
- van de Geer, S. (2014).Worst possible sub-directions in high-dimensional models.
Preprint available at ArXiv
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