[Back]

B. Pötscher, U. Schneider:
"Distributional Results for Thresholding Estimators in High-Dimensional Guassian Regression Models";
Talk: 28th Annual Congress of the European Economic Association & 67th European Meeting of the Econometric Society, Goteborg, Schweden; 2013-08-26 - 2013-08-30.

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 and may diverge with n. In addition to the case of known error-variance, we define and study versions of the estimators when the error-variance is unknown. We derive the finite-sample distribution of each estimator and study its behavior in the large-
sample limit, also investigating the effects of having to estimate the variance when the degrees of freedom n − k does not tend to infinity or tends to infinity very slowly. Our analysis encompasses both the case where the estimators are tuned to perform consistent variable selection and the case where the estimators are tuned to perform conservative variable selection.
Furthermore, we discuss consistency, uniform consistency and derive the uniform convergence rate under either type of tuning.

Thresholding, Lasso, adaptive Lasso, penalized maximum likelihood, finite-sample distribution, asymptotic distribution, variance estimation, uniform convergence rate, high-dimensional model, or- acle property, variable selection.