Small Area Estimation Under Informative Sampling
Small Area Estimation Under Informative Sampling
This article proposes two approaches for small area estimation under informative sampling. The semi-parametric approach makes no assumptions regarding the relationship between the area selection probabilities and the true area means. The proposed predictors under this approach are approximately unbiased for both the sampled and nonsampled areas but the prediction RMSEs can be large particularly for nonsampled areas. The parametric approach models the relationship between the area selection probabilities and the area means and incorporates this relationship into the model for the study variable. As illustrated by a simulation study, the use of this approach can reduce the RMSEs quite significantly.
Southampton Statistical Sciences Research Institute, University of Southampton
Pfeffermann, Danny
c7fe07a0-9715-42ce-b90b-1d4f2c2c6ffc
Sverchkov, Michail
425615f0-784c-4e6f-8108-2c50e1f4cb42
2003
Pfeffermann, Danny
c7fe07a0-9715-42ce-b90b-1d4f2c2c6ffc
Sverchkov, Michail
425615f0-784c-4e6f-8108-2c50e1f4cb42
Pfeffermann, Danny and Sverchkov, Michail
(2003)
Small Area Estimation Under Informative Sampling
(S3RI Methodology Working Papers, M03/22)
Southampton, UK.
Southampton Statistical Sciences Research Institute, University of Southampton
13pp.
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Monograph
(Project Report)
Abstract
This article proposes two approaches for small area estimation under informative sampling. The semi-parametric approach makes no assumptions regarding the relationship between the area selection probabilities and the true area means. The proposed predictors under this approach are approximately unbiased for both the sampled and nonsampled areas but the prediction RMSEs can be large particularly for nonsampled areas. The parametric approach models the relationship between the area selection probabilities and the area means and incorporates this relationship into the model for the study variable. As illustrated by a simulation study, the use of this approach can reduce the RMSEs quite significantly.
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Published date: 2003
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Local EPrints ID: 8172
URI: http://eprints.soton.ac.uk/id/eprint/8172
PURE UUID: 5cdfe8a7-9bc8-4931-a194-37c92e8a5329
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Date deposited: 11 Jul 2004
Last modified: 15 Mar 2024 04:51
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Author:
Michail Sverchkov
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