SLOPE-ADAPTIVE VARIABLE SELECTION VIA CONVEX OPTIMIZATION.

ABSTRACT: We introduce a new estimator for the vector of coefficients β in the linear model y = Xβ + z, where X has dimensions n × p with p possibly larger than n. SLOPE, short for Sorted L-One Penalized Estimation, is the solution to [Formula: see text]where λ₁ ≥ λ₂ ≥ … ≥ λ _p ≥ 0 and [Formula: see text] are the decreasing absolute values of the entries of b. This is a convex program and we demonstrate a solution algorithm whose computational complexity is roughly comparable to that of classical ℓ₁ procedures such as the Lasso. Here, the regularizer is a sorted ℓ₁ norm, which penalizes the regression coefficients according to their rank: the higher the rank-that is, stronger t

SUBMITTER: Bogdan M 

PROVIDER: S-EPMC4689150 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature