Ell;1-penalized ruantile regression in high-himensional sparse models

Published

Journal Article

We consider median regression and, more generally, a possibly infinite collection of quantile regressions in high-dimensional sparse models. In these models, the number of regressors p is very large, possibly larger than the sample size n, but only at most s regressors have a nonzero impact on each conditional quantile of the response variable, where s grows more slowly than n. Since ordinary quantile regression is not consistent in this case, we consider ℓ1-penalized quantile regression (ℓ1-QR), which penalizes the ℓ1-norm of regression coefficients, as well as the post-penalized QR estimator (post-ℓ1- QR), which applies ordinary QR to the model selected by ℓ1-QR. First, we show that under general conditions ℓ1-QR is consistent at the near-oracle rate √s/n√log(p v n), uniformly in the compact set u c (0, 1) of quantile indices. In deriving this result, we propose a partly pivotal, data-driven choice of the penalty level and show that it satisfies the requirements for achieving this rate. Second, we show that under similar conditions post-ℓ1-QR is consistent at the near-oracle rate √s/n√log(p v n), uniformly over u, even if the ℓ1-QR- selected models miss some components of the true models, and the rate could be even closer to the oracle rate otherwise. Third, we characterize conditions under which ℓ1-QR contains the true model as a submodel, and derive bounds on the dimension of the selected model, uniformly over u; we also provide conditions under which hard-thresholding selects the minimal true model, uniformly over u. © Institute of Mathematical Statistics, 2011.

Full Text

Duke Authors

Cited Authors

  • Belloni, A; Chernozhukov, V

Published Date

  • February 1, 2011

Published In

Volume / Issue

  • 39 / 1

Start / End Page

  • 82 - 130

International Standard Serial Number (ISSN)

  • 0090-5364

Digital Object Identifier (DOI)

  • 10.1214/10-AOS827

Citation Source

  • Scopus