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- Author: Verena Puchner
- Publisher: Springer Spektrum
- Year: 2014
- Pages: 101
- ISBN-10: 3658082232
- ISBN-13: 9783658082239
- Format: 14.8 x 21 x 0.7 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
Evaluation of Statistical Matching and Selected Sae Methods (e-book) (used book) | bookbook.eu
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Description
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.
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- Author: Verena Puchner
- Publisher: Springer Spektrum
- Year: 2014
- Pages: 101
- ISBN-10: 3658082232
- ISBN-13: 9783658082239
- Format: 14.8 x 21 x 0.7 cm, minkšti viršeliai
- Language: English English
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.
Reviews