Towards a General Large Sample Theory for Regularized Estimators

Michael Jansson; Demian Pouzo

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arXiv · 1712.07248

Towards a General Large Sample Theory for Regularized Estimators

Michael Jansson·Demian Pouzo

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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.

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Michael Jansson, Demian Pouzo. 2017-12-19. Towards a General Large Sample Theory for Regularized Estimators. https://arxiv.org/abs/1712.07248

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