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  • In this paper, we consider the problem of producing estimates of poverty and inequality measures using a Bayesian unit-level small area model, specified on the logarithmic transformation of the equivalised in come. In this framework, we extend the classical log-normal model to a finite mixture of log-normal distributions. Moreover, possible negative val ues are also accomodated. Notoriously, posterior moments for quantities in the original data scale are not necessarily finite under the log-normal model: to solve this problem, we propose a prior specification that guar antees their existence. These methods are applied to Italian data from the EU-SILC survey, complemented with Census information. As domains, we consider sub-population given by administrative provinces by gender. (xsd:string)
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  • (SILC) (xsd:string)
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  • EU-SILC-Bibliography (xsd:string)
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  • 2021 (xsd:gyear)
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  • 2021 (xsd:gyear)
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  • 64 (xsd:string)
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  • Unit level models on the log-scale: a new Bayesian proposal for poverty mapping (xsd:string)
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  • inproceedings (xsd:string)
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  • SAE 2021 BIG4small - Book of short papers (xsd:string)
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  • In SAE 2021 BIG4small - Book of short papers, edited by Michele, D’Alò and Falorsi, Stefano and Fasulo, Andrea, 64-69, 2021 (xsd:string)
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  • European Union Statistics on Income and Living Conditions (EU-SILC) (xsd:string)
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  • 2021 (xsd:string)
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  • SILC (xsd:string)
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  • 69 (xsd:string)
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