In this paper we study post-penalized estimators which apply ordinary, unpenalized linear regression to the model … selected by first-step penalized estimators, typically LASSO. It is well known that LASSO can estimate the regression function … the LASSO-based model selection fails” in the sense of missing some components of the true” regression model. By the true …

We consider median regression and, more generally, quantile regression in high-dimensional sparse models. In these … case the ordinary quantile regression is not consistent, we consider quantile regression penalized by the L1-norm of … that applies ordinary quantile regression to the selected model. Fifth, we evaluate the performance of L1-QR in a Monte …

In this paper we develop procedures for performing inference in regression models about how potential policy … on, and our theory covers, all main regression approaches for modeling and estimating conditional distributions, focusing …

regression ; mean regression ; series ; locally linear ; kernel methods …