Evaluation of Statistical Matching and Selected SAE Methods: Using Micro Census and EU-SILC Data (BestMasters) - Softcover

Puchner, Verena

Softcover

ISBN 10: 3658082232 ISBN 13: 9783658082239

Publisher: Springer Spektrum, 2014

Synopsis

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 this title

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.

From the Back Cover

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.

Edition details

• Publisher: Springer Spektrum
• Publication date: 2014
• Language: English
• ISBN 10: 3658082232
• ISBN 13: 9783658082239
• Binding: Paperback
• Number of pages: 114