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Small area estimation under spatial nonstationarity

Record type: Article

A geographical weighted empirical best linear unbiased predictor (GWEBLUP) for a small area average is proposed, and an estimator of its conditional mean squared error is developed. The popular empirical best linear unbiased predictor under the linear mixed model is obtained as a special case of the GWEBLUP. Empirical results using both model-based and design-based simulations, with the latter based on two real data sets, show that the GWEBLUP predictor can lead to efficiency gains when spatial nonstationarity is present in the data. A practical gain from using the GWEBLUP is in small area estimation for out of sample areas. In this case the efficient use of geographical information can potentially improve upon conventional synthetic estimation.

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Citation

Chandra, Hukum, Salvati, Nicola, Chambers, Ray and Tzavidis, Nikos (2012) Small area estimation under spatial nonstationarity [in special issue: Small Area Estimation] Computational Statistics & Data Analysis, 56, (10), pp. 2875-2888. (doi:10.1016/j.csda.2012.02.006).

More information

Submitted date: December 2011
e-pub ahead of print date: 15 February 2012
Published date: October 2012
Additional Information: 3rd Special Issue on Optimization Heuristics in Estimation and Modelling Problems
Keywords: borrowing strength over space, geographical weighted regression, out of sample small area estimation, spatial analysis
Organisations: Social Statistics

Identifiers

Local EPrints ID: 181967
URI: http://eprints.soton.ac.uk/id/eprint/181967
ISSN: 0167-9473
PURE UUID: 3eaa55b4-7c32-4ebe-93e7-98bd97845d14

Catalogue record

Date deposited: 27 Apr 2011 14:42
Last modified: 18 Jul 2017 11:58

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Contributors

Author: Hukum Chandra
Author: Nicola Salvati
Author: Ray Chambers
Author: Nikos Tzavidis

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