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Descrizione libro Soft Cover. Condizione: new. Codice articolo 9783658082239
Descrizione libro Condizione: New. Codice articolo ABLIING23Mar3113020244554
Descrizione libro Condizione: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book. Codice articolo ria9783658082239_lsuk
Descrizione libro Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 116 pp. Englisch. Codice articolo 9783658082239
Descrizione libro Paperback. Condizione: Brand New. 2015 edition. 116 pages. 8.27x5.83x0.40 inches. In Stock. Codice articolo x-3658082232
Descrizione libro Condizione: New. Book is in NEW condition. Codice articolo 3658082232-2-1
Descrizione libro Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Codice articolo 9783658082239
Descrizione libro Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Study in the field of technical sciencesRegression 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 compar. Codice articolo 5123837