random forests, lasso, ridge, deep neural nets, boosted trees, as well as various hybrids and aggregates of these methods (e ….g. a hybrid of a random forest and lasso). We illustrate the application of the general theory through application to the …

for a continuum of target parameters and for Lasso-type or Post-Lasso type methods to be used as estimators of a continuum … continua of Lasso or Post-Lasso type estimators for continua of (nuisance) regression functions and provide practical …

the Lasso estimator of regression coefficients as well as the threshold parameter. Our Lasso estimator not only selects …-asymptotic oracle inequalities for both the prediction risk and the l1 estimation loss for regression coefficients. Since the Lasso …

We develop results for the use of LASSO and Post-LASSO methods to form first-stage predictions and estimate optimal … provide conditions under which these estimators are asymptotically oracle-efficient. In simulation experiments, the LASSO …. In an empirical example dealing with the effect of judicial eminent domain decisions on economic outcomes, the LASSO …

selected by first-step penalized estimators, typically LASSO. It is well known that LASSO can estimate the regression function … at nearly the oracle rate, and is thus hard to improve upon. We show that post-LASSO performs at least as well as LASSO … the LASSO-based model selection fails” in the sense of missing some components of the true” regression model. By the true …

continuum of target parameters is of interest and the Lasso-type or post-Lasso type methods are used to estimate a continuum of … establish rate and consistency results for continua of Lasso or post-Lasso type methods for estimating continua of the (nuisance …

of large-scale regressions with LASSO is applied to reduce the dimensionality, and an overall penalty level is carefully …