37 citations · 91 across the 11 of their papers we have counts for
11 papers
Beyond the Oracle Property: Adaptive LASSO in Cointegrating Regressions with Local-to-Unity Regressors
Karsten Reichold, Ulrike Schneider
This paper derives new asymptotic results for the adaptive LASSO estimator in cointegrating regressions, allowing for uncertainty about whether the regressors are exact unit root p…
A Unified Framework for Pattern Recovery in Penalized and Thresholded Estimation and its Geometry
Piotr Graczyk, Ulrike Schneider, Tomasz Skalski +1
We consider the framework of penalized estimation where the penalty term is given by a real-valued polyhedral gauge, which encompasses methods such as LASSO, generalized LASSO, SLO…
Uniformly Valid Inference Based on the Lasso in Linear Mixed Models
Peter Kramlinger, Ulrike Schneider, Tatyana Krivobokova
Linear mixed models (LMMs) are suitable for clustered data and are common in biometrics, medicine, survey statistics and many other fields. In those applications, it is essential t…
The Geometry of Uniqueness, Sparsity and Clustering in Penalized Estimation
Ulrike Schneider, Patrick Tardivel
We provide a necessary and sufficient condition for the uniqueness of penalized least-squares estimators whose penalty term is given by a norm with a polytope unit ball, covering a…
Uniform Asymptotics and Confidence Regions Based on the Adaptive Lasso with Partially Consistent Tuning
Nicolai Amann, Ulrike Schneider
We consider the adaptive Lasso estimator with componentwise tuning in the framework of a low-dimensional linear regression model. In our setting, at least one of the components is…
On the Distribution, Model Selection Properties and Uniqueness of the Lasso Estimator in Low and High Dimensions
Karl Ewald, Ulrike Schneider
We derive expressions for the finite-sample distribution of the Lasso estimator in the context of a linear regression model in low as well as in high dimensions by exploiting the s…
Uniformly Valid Confidence Sets Based on the Lasso
Karl Ewald, Ulrike Schneider
In a linear regression model of fixed dimension , we construct confidence regions for the unknown parameter vector based on the Lasso estimator that uniformly and exactly…
Confidence Sets Based on Thresholding Estimators in High-Dimensional Gaussian Regression Models
Ulrike Schneider
We study confidence intervals based on hard-thresholding, soft-thresholding, and adaptive soft-thresholding in a linear regression model where the number of regressors may depe…
Distributional Results for Thresholding Estimators in High-Dimensional Gaussian Regression Models
Benedikt M. Pötscher, Ulrike Schneider
We study the distribution of hard-, soft-, and adaptive soft-thresholding estimators within a linear regression model where the number of parameters k can depend on sample size n a…
Confidence Sets Based on Penalized Maximum Likelihood Estimators in Gaussian Regression
Benedikt M. Pötscher, Ulrike Schneider
Confidence intervals based on penalized maximum likelihood estimators such as the LASSO, adaptive LASSO, and hard-thresholding are analyzed. In the known-variance case, the finite-…
On the Distribution of the Adaptive LASSO Estimator
Benedikt M. Pötscher, Ulrike Schneider
We study the distribution of the adaptive LASSO estimator (Zou (2006)) in finite samples as well as in the large-sample limit. The large-sample distributions are derived both for t…