Arbeitspapier
Regularized area-level modelling for robust small area estimation in the presence of unknown covariate measurement errors
An approach to model-based small area estimation under covariate measurement errors is presented. Using a min-max approach, we proof that regularized regression coefficient estimation is equivalent to robust optimization under additive noise. Applying this equivalence, the Fay-Herriot model is extended by l1-norm, squared l2-norm and elastic net regularizations as robustification against design matrix perturbations. This allows for reliable area-statistic estimates without distributive information about the measurement errors. A best predictor and a Jackknife estimator of the mean squared error are presented. The methodology is evaluated in a simulation study under multiple measurement error scenarios to support the theoretical findings. A comparison to other robust small area approaches is conducted. An empirical application to poverty mapping in the US is provided. Estimated economic figures from the US Census Bureau and crime records from the Uniform Crime Reporting Program are used to model the number of citizens below the federal poverty threshold.
- Language
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Englisch
- Bibliographic citation
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Series: Research Papers in Economics ; No. 4/19
- Classification
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Wirtschaft
- Subject
-
min-max
pathwise coordinate descent
regularized least squares
robust optimization
- Event
-
Geistige Schöpfung
- (who)
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Burgard, Jan Pablo
Krause, Joscha
Kreber, Dennis
- Event
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Veröffentlichung
- (who)
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Universität Trier, Fachbereich IV - Volkswirtschaftslehre
- (where)
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Trier
- (when)
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2019
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Burgard, Jan Pablo
- Krause, Joscha
- Kreber, Dennis
- Universität Trier, Fachbereich IV - Volkswirtschaftslehre
Time of origin
- 2019
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Regularized Regression Incorporating Network Information: Simultaneous Estimation of Covariate Coefficients and Connection Signs
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