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- Publications
- Influence
p-Values for High-Dimensional Regression
- N. Meinshausen, Lukas Meier, Peter Bühlmann
- Mathematics
- 13 November 2008
Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of noise variables. Asymptotically valid… Expand
Consistent neighbourhood selection for sparse high-dimensional graphs with the Lasso
- N. Meinshausen, Peter Bühlmann
- Mathematics
- 2004
The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between variables. The structure is most… Expand
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- PDF
DISCUSSION OF: TREELETS—AN ADAPTIVE MULTI-SCALE BASIS FOR SPARSE UNORDERED DATA
- N. Meinshausen, Peter Bühlmann
- Mathematics
- 1 June 2008
We congratulate Lee, Nadler and Wasserman (henceforth LNW) on a very interesting paper on new methodology and supporting theory. Treelets seem to tackle two important problems of modern data analysis… Expand
Upper bounds for the number of true null hypotheses and novel estimates for error rates in multiple testing
- N. Meinshausen, Peter Bühlmann
- Mathematics
- 2004
When testing multiple hypotheses simultaneously, a quantity of interest is the number m0 of true null hypotheses. We present a general framework for finding upper probabilistic bounds for m0, that is… Expand