L1-Penalized Quantile Regression in High Dimensional Sparse Models

56 citations recorded by the source corpus.

Citations in the corpus

Uniform post-selection inference for least absolute deviation regression and other Z-estimation problems (2013, unknown): “Therefore it does rely on l1-LAD and Lasso obtaining sparse solutions which in turn typically relies on restricted isometry conditions [3, 2].”
Double/debiased machine learning for treatment and structural parameters (2017, unknown): “See, among many others, Bickel et al. (2009), Bühlmann and van de Geer (2011), Belloni et al. (2011), Belloni and Chernozhukov (2011), Belloni et al. (2012), and Belloni and Chernozhukov (2013) for `1-penalized and related methods in a variety of sparse models; Kozbur (2016) for forward selection…”
Systemic risk spillovers in the European banking and sovereign network (2016, unknown): “In particular, we use a weighted version of the least absolute shrinkage and selection operator (LASSO) approach for quantile regression as introduced by Belloni and Chernozhukov (2011).”
Jackknife model averaging for quantile regressions (2015, unknown): “More recently, Wu and Liu (2009) study variable selection in penalized QR (see Su and Zhang (2014) for an overview on this); Belloni and Chernozhukov (2011) consider 1-penalized QR in high-dimensional sparse models.”
Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations (2013, unknown): “7Examples include van de Geer (2008), Belloni and Chernozhukov (2011a), or Negahban, Ravikumar, Wainwright, and Yu (2012).”