Small area estimation for spatially correlated populations - a comparison of direct and indirect model-based methods
Small area estimation for spatially correlated populations - a comparison of direct and indirect model-based methods
Linear mixed models underpin many small area estimation (SAE) methods. In this paper we investigate SAE based on linear models with spatially correlated small area effects where the neighbourhood structure is described by a contiguity matrix. Such models allow efficient use of spatial auxiliary information in SAE. In particular, we use simulation studies to compare the performances of model-based direct estimation (MBDE) and empirical best linear unbiased prediction (EBLUP) under such models. These simulations are based on theoretically generated populations as well as data obtained from two real populations (the ISTAT farm structure survey in Tuscany and the US Environmental Monitoring and Assessment Program survey). Our empirical results show only marginal gains when spatial dependence between areas is incorporated into the SAE model.
University of Southampton
Chandra, Hukum
20235c19-9d73-47d0-abcc-65d9d8cc716c
Salvati, Nicola
9be298e5-de55-4a24-9361-054a2ec09726
Chambers, Ray
96331700-f45e-4483-a887-fef921888ff2
19 April 2007
Chandra, Hukum
20235c19-9d73-47d0-abcc-65d9d8cc716c
Salvati, Nicola
9be298e5-de55-4a24-9361-054a2ec09726
Chambers, Ray
96331700-f45e-4483-a887-fef921888ff2
Chandra, Hukum, Salvati, Nicola and Chambers, Ray
(2007)
Small area estimation for spatially correlated populations - a comparison of direct and indirect model-based methods
(S3RI Methodology Working Papers, M07/09)
Southampton, GB.
University of Southampton
25pp.
Record type:
Monograph
(Working Paper)
Abstract
Linear mixed models underpin many small area estimation (SAE) methods. In this paper we investigate SAE based on linear models with spatially correlated small area effects where the neighbourhood structure is described by a contiguity matrix. Such models allow efficient use of spatial auxiliary information in SAE. In particular, we use simulation studies to compare the performances of model-based direct estimation (MBDE) and empirical best linear unbiased prediction (EBLUP) under such models. These simulations are based on theoretically generated populations as well as data obtained from two real populations (the ISTAT farm structure survey in Tuscany and the US Environmental Monitoring and Assessment Program survey). Our empirical results show only marginal gains when spatial dependence between areas is incorporated into the SAE model.
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Published date: 19 April 2007
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Local EPrints ID: 45874
URI: http://eprints.soton.ac.uk/id/eprint/45874
PURE UUID: b478663b-5070-4398-8582-f02fa7a6c14c
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Date deposited: 19 Apr 2007
Last modified: 09 Nov 2021 08:51
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Author:
Hukum Chandra
Author:
Nicola Salvati
Author:
Ray Chambers
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