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Evaluation of statistical matching and selected SAE methods[electronic resource] :using micro census and EU-SILC data /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	339.460727
書名/作者:	Evaluation of statistical matching and selected SAE methods : using micro census and EU-SILC data // by Verena Puchner.
作者:	Puchner, Verena.
出版者:	Wiesbaden : : Springer Fachmedien Wiesbaden :, 2015.
面頁冊數:	xiii, 101 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Poverty - Statistical methods.
標題:	Mathematics.
標題:	Computational Mathematics and Numerical Analysis.
標題:	Probability Theory and Stochastic Processes.
標題:	Applications of Mathematics.
ISBN:	9783658082246 (electronic bk.)
ISBN:	9783658082239 (paper)
內容註:	Regression Models Including Selected Small Area Methods -- Statistical Matching -- Application to Poverty Estimation Using EU-SILC and Micro Census Data -- Bootstrap Methods.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-658-08224-6

Evaluation of statistical matching and selected SAE methods[electronic resource] :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:

603772
Poverty
--Statistical methods.

LC Class. No.: HC79.P6

Dewey Class. No.: 339.460727

Evaluation of statistical matching and selected SAE methods[electronic resource] :using micro census and EU-SILC data /
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