In this paper we specify, within a hierarchical Bayesian setting, appropriate atom-based models to solve the following small area estimation (SAE) questions: (i) combining auxiliary covariates which are available on non nested areal partitions (misaligned areal regression problem); (ii) providing small area estimates by using planned domains data (misaligned areal interpolation problem). To illustrate our approach we consider the problem of estimating the number of unemployed at Local Labour Market area (small area or target zone) level by using two misaligned source data: auxiliary information available on different administrative partitions; reliable estimates of unemployed on Labour Force Survey planned domains. Thus we explore the close connection that typical SAE issues show to have with spatial misalignment problems. Ob ject of SAE is, in fact, inference on survey non-planned “minor domains” (the so called small areas): based on direct domain data (when available), it leads to estimates of poor quality. Thereby models are set up for borrowing strength from indirectly related data sources. Similarly, spatial misalignment models are set up whenever “target zones” for which data are needed are different from source zones on which data are available.

Spatial misalignment modeling for small area estimation problems

TREVISANI, MATILDE;TORELLI, Nicola
2005-01-01

Abstract

In this paper we specify, within a hierarchical Bayesian setting, appropriate atom-based models to solve the following small area estimation (SAE) questions: (i) combining auxiliary covariates which are available on non nested areal partitions (misaligned areal regression problem); (ii) providing small area estimates by using planned domains data (misaligned areal interpolation problem). To illustrate our approach we consider the problem of estimating the number of unemployed at Local Labour Market area (small area or target zone) level by using two misaligned source data: auxiliary information available on different administrative partitions; reliable estimates of unemployed on Labour Force Survey planned domains. Thus we explore the close connection that typical SAE issues show to have with spatial misalignment problems. Ob ject of SAE is, in fact, inference on survey non-planned “minor domains” (the so called small areas): based on direct domain data (when available), it leads to estimates of poor quality. Thereby models are set up for borrowing strength from indirectly related data sources. Similarly, spatial misalignment models are set up whenever “target zones” for which data are needed are different from source zones on which data are available.
2005
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/1844889
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