Small area estimation under spatial nonstationarity
Small area estimation under spatial nonstationarity
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.
borrowing strength over space, geographical weighted regression, out of sample small area estimation, spatial analysis
2875-2888
Chandra, Hukum
20235c19-9d73-47d0-abcc-65d9d8cc716c
Salvati, Nicola
9be298e5-de55-4a24-9361-054a2ec09726
Chambers, Ray
96331700-f45e-4483-a887-fef921888ff2
Tzavidis, Nikos
431ec55d-c147-466d-9c65-0f377b0c1f6a
October 2012
Chandra, Hukum
20235c19-9d73-47d0-abcc-65d9d8cc716c
Salvati, Nicola
9be298e5-de55-4a24-9361-054a2ec09726
Chambers, Ray
96331700-f45e-4483-a887-fef921888ff2
Tzavidis, Nikos
431ec55d-c147-466d-9c65-0f377b0c1f6a
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), .
(doi:10.1016/j.csda.2012.02.006).
Abstract
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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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
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Local EPrints ID: 181967
URI: http://eprints.soton.ac.uk/id/eprint/181967
ISSN: 0167-9473
PURE UUID: 3eaa55b4-7c32-4ebe-93e7-98bd97845d14
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Date deposited: 27 Apr 2011 14:42
Last modified: 15 Mar 2024 03:11
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Author:
Hukum Chandra
Author:
Nicola Salvati
Author:
Ray Chambers
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