L1 regularization, or regularization with an L1 penalty, is a popular idea in statistics and
machine learning. This paper reviews the concept and application of L1 regularization for …

It is common practice in statistical data analysis to perform data-driven variable selection
and derive statistical inference from the resulting model. Such inference enjoys none of the …

In this article, we consider bootstrapping the Lasso estimator of the regression parameter in
a multiple linear regression model. It is known that the standard bootstrap method fails to be …

We study the distributions of the LASSO, SCAD, and thresholding estimators, in finite
samples and in the large-sample limit. The asymptotic distributions are derived for both the …

Rates of convergence of the Adaptive LASSO estimators to the Oracle distribution and higher
order refinements by the bootstrap Page 1 The Annals of Statistics 2013, Vol. 41, No. 3 …

We study the asymptotic properties of Lasso+ mLS and Lasso+ Ridge under the sparse high-
dimensional linear regression model: Lasso selecting predictors and then modified Least …

Analysis of high-dimensional data often seeks to identify a subset of important features and
to assess the effects of these features on outcomes. Traditional statistical inference …

We consider the problem of determining the number of factors and selecting the proper
regressors in linear dynamic panel data models with interactive fixed effects. Based on the …

This paper studies the estimation of a panel data model with latent structures where
individuals can be classified into different groups with the slope parameters being …

We show that the adaptive Lasso is oracle efficient in stationary and nonstationary
autoregressions. This means that it estimates parameters consistently, selects the correct …