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Evaluation of Statistical Matching and Selected SAE Methods

Verena Puchner

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ISBN 10: 3658082232 / ISBN 13: 9783658082239
Editore: Betriebswirt.-Vlg Gabler Dez 2014, 2014
Nuovi Condizione: Neu Brossura
Da Rhein-Team Lörrach Ivano Narducci e.K. (Lörrach, Germania)

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Neuware - Verena Puchner evaluates and compares statistical matching and selected SAE methods. Due to the fact that poverty estimation at regional level based on EU-SILC samples is not of adequate accuracy, the quality of the estimations should be improved by additionally incorporating micro census data. The aim is to find the best method for the estimation of poverty in terms of small bias and small variance with the aid of a simulated artificial 'close-to-reality' population. Variables of interest are imputed into the micro census data sets with the help of the EU-SILC samples through regression models including selected unit-level small area methods and statistical matching methods. Poverty indicators are then estimated. The author evaluates and compares the bias and variance for the direct estimator and the various methods. The variance is desired to be reduced by the larger sample size of the micro census. 101 pp. Englisch. Codice inventario libreria 9783658082239

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Dati bibliografici

Titolo: Evaluation of Statistical Matching and ...

Casa editrice: Betriebswirt.-Vlg Gabler Dez 2014

Data di pubblicazione: 2014

Legatura: Taschenbuch

Condizione libro:Neu

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Riassunto:

Verena Puchner evaluates and compares statistical matching and selected SAE methods. Due to the fact that poverty estimation at regional level based on EU-SILC samples is not of adequate accuracy, the quality of the estimations should be improved by additionally incorporating micro census data. The aim is to find the best method for the estimation of poverty in terms of small bias and small variance with the aid of a simulated artificial "close-to-reality" population. Variables of interest are imputed into the micro census data sets with the help of the EU-SILC samples through regression models including selected unit-level small area methods and statistical matching methods. Poverty indicators are then estimated. The author evaluates and compares the bias and variance for the direct estimator and the various methods. The variance is desired to be reduced by the larger sample size of the micro census.

About the Author:

Verena Puchner obtained her master’s degree at Technical University of Vienna under the supervision of Priv.-Doz. Dipl.-Ing. Dr. techn. Matthias Templ. At present, she works as a data miner and consultant.

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