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p-Values for High-Dimensional Regression
Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of noise variables. Asymptotically validExpand
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Consistent neighbourhood selection for sparse high-dimensional graphs with the Lasso
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 mostExpand
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DISCUSSION OF: TREELETS—AN ADAPTIVE MULTI-SCALE BASIS FOR SPARSE UNORDERED DATA
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 analysisExpand
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Upper bounds for the number of true null hypotheses and novel estimates for error rates in multiple testing
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 isExpand
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