Evaluation of Statistical Matching and Selected SAE Methods [electronic resource] : Using Micro Census and EU-SILC Data /
Puchner, Verena.author.
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.
Description based upon print version of record.
Includes bibliographical references.
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.
Computer mathematics.
Probabilities.
Applied mathematics.
Engineering mathematics.
Computational Mathematics and Numerical Analysis.
Probability Theory and Stochastic Processes.
Applications of Mathematics.