Arbeitspapier
Towards a general large sample theory for regularized estimators
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 studying a wide range of applications, revisiting known results and deriving new ones.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP63/19
- Klassifikation
-
Wirtschaft
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Jansson, Michael
Pouzo, Demian
- Ereignis
-
Veröffentlichung
- (wer)
-
Centre for Microdata Methods and Practice (cemmap)
- (wo)
-
London
- (wann)
-
2019
- DOI
-
doi:10.1920/wp.cem.2019.6319
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Jansson, Michael
- Pouzo, Demian
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2019
Ähnliche Objekte (12)
Statistical guarantees for regularized (approximate) estimators in machine learning
Large sample properties for estimators based on the order statistics approach in auctions
Finite sample analysis of profile M-estimators
Finite sample accuracy of integrated volatility estimators
Conditional Quantile Estimators: A Small Sample Theory
SOME CLASSES OF MODIFIED RATIO TYPE ESTIMATORS IN SAMPLE SURVEYS
Looming Large or Seeming Small? Attitudes Towards Losses in a Representative Sample
Elements of large sample theory
Some Heteroskedasticity Consistent Covariance Matrix Estimators with Improved Finite Sample Properties
Finite sample inference for GMM estimators in linear panel data models
Large-sample inference on spatial dependence
Quantum tomography via compressed sensing: error bounds, sample complexity and efficient estimators
Statistical guarantees for regularized (approximate) estimators in machine learning
Large sample properties for estimators based on the order statistics approach in auctions
Finite sample analysis of profile M-estimators
Finite sample accuracy of integrated volatility estimators
Conditional Quantile Estimators: A Small Sample Theory
SOME CLASSES OF MODIFIED RATIO TYPE ESTIMATORS IN SAMPLE SURVEYS
Looming Large or Seeming Small? Attitudes Towards Losses in a Representative Sample
Elements of large sample theory
Some Heteroskedasticity Consistent Covariance Matrix Estimators with Improved Finite Sample Properties
Finite sample inference for GMM estimators in linear panel data models
Large-sample inference on spatial dependence
Quantum tomography via compressed sensing: error bounds, sample complexity and efficient estimators
Statistical guarantees for regularized (approximate) estimators in machine learning
Large sample properties for estimators based on the order statistics approach in auctions
Finite sample analysis of profile M-estimators
Finite sample accuracy of integrated volatility estimators
Conditional Quantile Estimators: A Small Sample Theory
SOME CLASSES OF MODIFIED RATIO TYPE ESTIMATORS IN SAMPLE SURVEYS
Looming Large or Seeming Small? Attitudes Towards Losses in a Representative Sample
Elements of large sample theory
Some Heteroskedasticity Consistent Covariance Matrix Estimators with Improved Finite Sample Properties
Finite sample inference for GMM estimators in linear panel data models
Large-sample inference on spatial dependence