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  • A model-based small area method for calculating estimates of poverty rates based on different thresholds for subsets of the Italian population is proposed. The subsets are obtained by cross-classifying by household type and administrative region. The suggested estimators satisfy the following coherence properties: (i) within a given area, rates associated with increasing thresholds are monotonically increasing; (ii) interval estimators have lower and upper bounds within the interval (0, 1); (iii) when a large domain-specific sample is available the small area estimate is close to the one obtained using standard design-based methods; (iv) estimates of poverty rates should also be produced for domains for which there is no sample or when no poor households are included in the sample. A hierarchical Bayesian approach to estimation is adopted. Posterior distributions are approximated by means of MCMC computation methods. Empirical analysis is based on data from the 2005 wave of the EU-SILC survey. (xsd:string)
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  • (SILC) (xsd:string)
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  • EU-SILC-Bibliography (xsd:string)
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  • 2011 (xsd:gyear)
?:datePublished
  • 2011 (xsd:gyear)
?:doi
  • 10.1016/j.csda.2010.11.001 ()
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  • 1736 (xsd:string)
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  • english (xsd:string)
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  • 0167-9473 ()
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  • 4 (xsd:string)
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  • Hierarchical Bayes multivariate estimation of poverty rates based on increasing thresholds for small domains (xsd:string)
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  • article (xsd:string)
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  • Bibsonomy (xsd:string)
  • In Computational Statistics and Data Analysis, 55(4), 1736-1747, 2011 (xsd:string)
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  • European Union Statistics on Income and Living Conditions (EU-SILC) (xsd:string)
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  • 2011 (xsd:string)
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  • SCIEindexed (xsd:string)
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  • SILC (xsd:string)
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  • 1747 (xsd:string)
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  • 55 (xsd:string)