Retire: Robust Expectile Regression in High Dimensions
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High-dimensional data can often display heterogeneity due to heteroscedastic variance or inhomogeneous covariate effects. Penalized quantile and expectile regression methods offer useful tools to detect heteroscedasticity in high-dimensional data. The former is computationally challenging due to the non-smooth nature of the check loss, and the latter is sensitive to heavy-tailed error distributions. In this paper, we propose and study (penalized) robust expectile regression (retire), with a focus on iteratively reweighted ℓ_1-penalization which reduces the estimation bias from ℓ_1-penalization and leads to oracle properties. Theoretically, we establish the statistical properties of the retire estimator under two regimes: (i) low-dimensional regime in which d ≪ n; (ii) high-dimensional regime in which s≪ n≪ d with s denoting the number of significant predictors. In the high-dimensional setting, we carefully characterize the solution path of the iteratively reweighted ℓ_1-penalized retire estimation, adapted from the local linear approximation algorithm for folded-concave regularization. Under a mild minimum signal strength condition, we show that after as many as log(log d) iterations the final iterate enjoys the oracle convergence rate. At each iteration, the weighted ℓ_1-penalized convex program can be efficiently solved by a semismooth Newton coordinate descent algorithm. Numerical studies demonstrate the competitive performance of the proposed procedure compared with either non-robust or quantile regression based alternatives.
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