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Evaluation of statistical matching a...

Puchner, Verena.

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Evaluation of statistical matching and selected SAE methods = using micro census and EU-SILC data /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Evaluation of statistical matching and selected SAE methods/ by Verena Puchner.
Reminder of title:	using micro census and EU-SILC data /
Author:	Puchner, Verena.
Published:	Wiesbaden :Springer Fachmedien Wiesbaden : : 2015.,
Description:	xiii, 101 p. :ill., digital ;24 cm.
[NT 15003449]:	Regression Models Including Selected Small Area Methods -- Statistical Matching -- Application to Poverty Estimation Using EU-SILC and Micro Census Data -- Bootstrap Methods.
Contained By:	Springer eBooks
Subject:	Poverty - Statistical methods. -
Online resource:	http://dx.doi.org/10.1007/978-3-658-08224-6
ISBN:	9783658082246 (electronic bk.)

Evaluation of statistical matching and selected SAE methods = using micro census and EU-SILC data /
Puchner, Verena.

Evaluation of statistical matching and selected SAE methodsusing micro census and EU-SILC data /[electronic resource] :by Verena Puchner. - Wiesbaden :Springer Fachmedien Wiesbaden :2015. - xiii, 101 p. :ill., digital ;24 cm. - BestMasters. - BestMasters..

Regression Models Including Selected Small Area Methods -- Statistical Matching -- Application to Poverty Estimation Using EU-SILC and Micro Census Data -- Bootstrap Methods.

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. Contents Regression Models Including Selected Small Area Methods Statistical Matching Application to Poverty Estimation Using EU-SILC and Micro Census Data Bootstrap Methods Target Groups Researchers, students, and practitioners in the fields of statistics, official statistics, and survey statistics 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.

ISBN: 9783658082246 (electronic bk.)

Standard No.: 10.1007/978-3-658-08224-6doiSubjects--Topical Terms:

797094
Poverty
--Statistical methods.

LC Class. No.: HC79.P6

Dewey Class. No.: 339.460727

Evaluation of statistical matching and selected SAE methods = using micro census and EU-SILC data /
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505 0 $a Regression Models Including Selected Small Area Methods -- Statistical Matching -- Application to Poverty Estimation Using EU-SILC and Micro Census Data -- Bootstrap Methods.
520 $a 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. Contents Regression Models Including Selected Small Area Methods Statistical Matching Application to Poverty Estimation Using EU-SILC and Micro Census Data Bootstrap Methods Target Groups Researchers, students, and practitioners in the fields of statistics, official statistics, and survey statistics 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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