Small-area estimation with spatial similarity
A class of composite estimators of small area quantities that exploit spatial (distancerelated) 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.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Paul Elliott & Jon Wakefield, 2001. "Disease clusters: should they be investigated, and, if so, when and how?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 3-12.
When requesting a correction, please mention this item's handle: RePEc:upf:upfgen:1105. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ()
If references are entirely missing, you can add them using this form.