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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.
Mathematics. --- Computational Mathematics and Numerical Analysis. --- Probability Theory and Stochastic Processes. --- Applications of Mathematics. --- Computer science --- Distribution (Probability theory). --- Mathématiques --- Informatique --- Distribution (Théorie des probabilités) --- Computer science -- Mathematics. --- Poverty -- Statistical methods. --- Mathematics --- Physical Sciences & Mathematics --- Mathematics - General --- Statistical matching. --- Statistics --- Formal methods (Computer science) --- Income distribution --- Poverty --- Social sciences --- Data processing. --- Research --- Mathematical models. --- Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Destitution --- Distribution of income --- Income inequality --- Inequality of income --- Concatenation, File (Statistics) --- Data fusion (Statistics) --- Data matching (Statistics) --- Data merging (Statistics) --- File concatenation (Statistics) --- Fusion, Data (Statistics) --- Imputation, Mass (Statistics) --- Mass imputation (Statistics) --- Matching, Data (Statistics) --- Matching, Statistical --- Merging, Data (Statistics) --- Microsimulation modeling (Statistics) --- Modeling, Microsimulation (Statistics) --- Applied mathematics. --- Engineering mathematics. --- Computer mathematics. --- Probabilities. --- Wealth --- Basic needs --- Begging --- Poor --- Subsistence economy --- Distribution (Economic theory) --- Disposable income --- System design --- Civilization --- Sampling (Statistics) --- Distribution (Probability theory. --- Computer mathematics --- Discrete mathematics --- Electronic data processing --- Math --- Science --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Engineering --- Engineering analysis --- Mathematical analysis --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Probability Theory.
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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.
Operational research. Game theory --- Probability theory --- Mathematics --- Applied physical engineering --- Computer. Automation --- toegepaste wiskunde --- waarschijnlijkheidstheorie --- stochastische analyse --- computers --- economie --- informatica --- wiskunde --- kansrekening
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