Post-[script l]\2081-penalized estimators in high-dimensional linear regression models
By: [by] Alexandre Belloni [and] Victor Chernozhukov .
Contributors: Chernozhukov, Victor; Massachusetts Institute of Technology. Dept. of Economics
Edition: Rev.
Series: Working paper series / Massachusetts Institute of Technology, Dept. of Economics -- working paper 10-5 [2010 revision]; Working paper (Massachusetts Institute of Technology. Dept. of Economics) -- no. 10-05, 2010.
Publisher: Massachusetts Institute of Technology, Dept. of Economics
Published: 2010
Place of publication: Cambridge, MA
Language: eng
Number of pages: 38
Other titles: Post-LASSO-penalized estimators in high-dimensional linear regression models
Description: 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 at nearly the oracle rate, and is thus hard to improve upon. We show that post-LASSO performs at least as well as LASSO in terms of the rate of convergence, and has the advantage of a smaller bias. Remarkably, this performance occurs even if the LASSO-based model selection fails in the sense of missing some components of the true regression model. By the true model we mean here the best s-dimensional approximation to the regression function chosen by the oracle. Furthermore, post-LASSO can perform strictly better than LASSO, in the sense of a strictly faster rate of convergence, if the LASSO-based model selection correctly includes all components of the true model as a subset and also achieves a sufficient sparsity. In the extreme case, when LASSO perfectly selects the true model, the post-LASSO estimator becomes the oracle estimator. An important ingredient in our analysis is a new sparsity bound on the dimension of the model selected by LASSO which guarantees that this dimension is at most of the same order as the dimension of the true model. Our rate results are non-asymptotic and hold in both parametric and nonparametric models. Moreover, our analysis is not limited to the LASSO estimator in the first step, but also applies to other estimators, for example, the trimmed LASSO, Dantzig selector, or any other estimator with good rates and good sparsity. Our analysis covers both traditional trimming and a new practical, completely data-driven trimming scheme that induces maximal sparsity subject to maintaining a certain goodness-of-fit. The latter scheme has theoretical guarantees similar to those of LASSO or post-LASSO, but it dominates these procedures as well as traditional trimming in a wide variety of experiments. Keywords: LASSO, post-LASSO, post-model-selection estimators. JEL Classifications: 62H12, 62J99, 62J07.
Notes: Cover title.

"January 4, 2009 -- Current Revision: March 29, 2010."

Includes bibliographical references (leaves 37-38).

Abstract in HTML and working paper for download in PDF available via World Wide Web at the Social Science Research Network.