MOCE constructs simultaneous confidence regions after LASSO by debiasing on an expanded model with a ridge-type precision matrix, claiming validity under a=o(n/log p) sparsity.
For a vector ν = (ν1,··· ,νp)T ∈ Rp, the 𝓁0-norm is ‖ν‖0 = ∑p j= 1{|νj| > 0}; the ∞-norm is ‖ν‖∞ = max 1≤j≤p |νj|; and the 𝓁2- norm is ‖ν‖2 2 = ∑p j=1ν2 j
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2019 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models
MOCE constructs simultaneous confidence regions after LASSO by debiasing on an expanded model with a ridge-type precision matrix, claiming validity under a=o(n/log p) sparsity.