739 citations · 1.8k across the 34 of their papers we have counts for
5 papers · 1 filter
Estimating high-dimensional intervention effects from observational data
Marloes H. Maathuis, Markus Kalisch, Peter Bühlmann
We assume that we have observational data generated from an unknown underlying directed acyclic graph (DAG) model. A DAG is typically not identifiable from observational data, but…
Discussion: One-step sparse estimates in nonconcave penalized likelihood models
Peter Bühlmann, Lukas Meier
Discussion of ``One-step sparse estimates in nonconcave penalized likelihood models'' [arXiv:0808.1012]
Discussion of: Treelets--An adaptive multi-scale basis for sparse unordered data
Nicolai Meinshausen, Peter Bühlmann
We congratulate Lee, Nadler and Wasserman (henceforth LNW) on a very interesting paper on new methodology and supporting theory [arXiv:0707.0481]. Treelets seem to tackle two impor…
Rejoinder: Boosting Algorithms: Regularization, Prediction and Model Fitting
Peter Bühlmann, Torsten Hothorn
Rejoinder to ``Boosting Algorithms: Regularization, Prediction and Model Fitting'' [arXiv:0804.2752]
Boosting Algorithms: Regularization, Prediction and Model Fitting
Peter Bühlmann, Torsten Hothorn
We present a statistical perspective on boosting. Special emphasis is given to estimating potentially complex parametric or nonparametric models, including generalized linear and a…