2017/12/19 by Michael Jansson, Jansson, Michael, Demián Pouzo +2
Computer Science · Economics, Econometrics and Finance · Engineering · Mathematics · #Control Systems and Identification #Econometrics (econ.EM) #FOS: Economics and business #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Statistical Methods and Inference #Statistics Theory (math.ST) #econ.EM #math.ST #stat.TH
paper · pdf · doi:10.48550/arxiv.1712.07248
openalex publication_date 2017/12/19 · arxiv created 2020/07/13 · arxiv updated 2020/07/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.