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Small area estimation with spatial similarity

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  • Longford, Nicholas T.

Abstract

Summary A class of composite estimators of small area quantities that exploit spatial (distance-related) similarity is derived. It is based on a distribution-free model for the areas, but the estimators are aimed to have optimal design-based properties. Composition is applied also to estimate some of the global parameters on which the small area estimators depend. It is shown that the commonly adopted assumption of random effects is not necessary for exploiting the similarity of the districts (borrowing strength across the districts). The methods are applied in the estimation of the mean household sizes and the proportions of single-member households in the counties (comarcas) of Catalonia. The simplest version of the estimators is more efficient than the established alternatives, even though the extent of spatial similarity is quite modest.

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  • Longford, Nicholas T., 2010. "Small area estimation with spatial similarity," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1151-1166, April.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:4:p:1151-1166
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    References listed on IDEAS

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    1. Kang, Emily L. & Liu, Desheng & Cressie, Noel, 2009. "Statistical analysis of small-area data based on independence, spatial, non-hierarchical, and hierarchical models," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3016-3032, June.
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    5. Temiyasathit, Chivalai & Kim, Seoung Bum & Park, Sun-Kyoung, 2009. "Spatial prediction of ozone concentration profiles," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3892-3906, September.
    6. Nicholas T. Longford, 2004. "Missing data and small area estimation in the UK Labour Force Survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(2), pages 341-373, May.
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    Cited by:

    1. Baldermann, Claudia & Salvati, Nicola & Schmid, Timo, 2016. "Robust small area estimation under spatial non-stationarity," Discussion Papers 2016/5, Free University Berlin, School of Business & Economics.
    2. Fabrizi, Enrico & Ferrante, Maria Rosaria & Pacei, Silvia & Trivisano, Carlo, 2011. "Hierarchical Bayes multivariate estimation of poverty rates based on increasing thresholds for small domains," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1736-1747, April.

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