Towards a General Large Sample Theory for Regularized Estimators
Statistics Theory
2020-07-14 v4 Econometrics
Statistics Theory
Abstract
We present a general framework for studying regularized estimators; such estimators are pervasive in estimation problems wherein "plug-in" type estimators are either ill-defined or ill-behaved. Within this framework, we derive, under primitive conditions, consistency and a generalization of the asymptotic linearity property. We also provide data-driven methods for choosing tuning parameters that, under some conditions, achieve the aforementioned properties. We illustrate the scope of our approach by presenting a wide range of applications.
Cite
@article{arxiv.1712.07248,
title = {Towards a General Large Sample Theory for Regularized Estimators},
author = {Michael Jansson and Demian Pouzo},
journal= {arXiv preprint arXiv:1712.07248},
year = {2020}
}