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We present a novel method for controlling the $k$-familywise error rate ($k$-FWER) in the linear regression setting using the knockoffs framework first introduced by Barber and Cand\`es.
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New procedures controlling the false discovery proportion via Romano-Wolf’s heuristic
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Exact post-selection inference with the Lasso
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Further results on controlling the false discovery proportion
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Confidence intervals and hypothesis testing for high-dimensional regression
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Stepup procedures for control of generalizations of the familywise error rate
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J. P. Romano and M. Wolf · 2007
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Multiple comparisons controlling expected number of false discoveries
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A significance test for the Lasso
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Controlling the false discovery rate via knockoffs
R. F. Barber and E. J. Candès · 2015
Closest in time.
SLOPE – Adaptive variable selection via convex optimization
M. Bogdan, E. v. d. Berg, C. Sabatti, W. Su, and E. J. Candès · 2015
Closest in time.
SLOPE is adaptive to unknown sparsity and asymptotically minimax
W. Su and E. J. Candès · 2015
Closest in time.
False discoveries occur early on the Lasso path
W. Su, M. Bogdan, and E. J. Candès · 2015
Closest in time.