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Estimation of randomisation mean square error in small area estimation

Estimation of randomisation mean square error in small area estimation
Estimation of randomisation mean square error in small area estimation

In this article, we propose a new method for estimating the randomisation (design-based) mean squared error (DMSE) of model-dependent small area predictors. Analogously to classical survey sampling theory, the DMSE considers the finite population values as fixed numbers and accounts for the MSE of small area predictors over all possible sample selections. The proposed method models the true DMSE as computed for synthetic populations and samples drawn from them, as a function of known statistics and then applies the model to the original sample. Several simulation studies for the linear area-level model and the unit-level mixed logistic model illustrate the performance of the proposed method and compare it with the performance of other DMSE estimators proposed in the literature.

Area-level model, design MSE, mixed logistic model, model-based MSE
0306-7734
1-19
Pfeffermann, Danny
c7fe07a0-9715-42ce-b90b-1d4f2c2c6ffc
Ben-Hur, Dano
1802479e-86c6-4fd3-a055-347cec184409
Pfeffermann, Danny
c7fe07a0-9715-42ce-b90b-1d4f2c2c6ffc
Ben-Hur, Dano
1802479e-86c6-4fd3-a055-347cec184409

Pfeffermann, Danny and Ben-Hur, Dano (2018) Estimation of randomisation mean square error in small area estimation. International Statistical Review, 1-19. (doi:10.1111/insr.12289).

Record type: Article

Abstract

In this article, we propose a new method for estimating the randomisation (design-based) mean squared error (DMSE) of model-dependent small area predictors. Analogously to classical survey sampling theory, the DMSE considers the finite population values as fixed numbers and accounts for the MSE of small area predictors over all possible sample selections. The proposed method models the true DMSE as computed for synthetic populations and samples drawn from them, as a function of known statistics and then applies the model to the original sample. Several simulation studies for the linear area-level model and the unit-level mixed logistic model illustrate the performance of the proposed method and compare it with the performance of other DMSE estimators proposed in the literature.

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DMSE 12-06 - Accepted Manuscript
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More information

Accepted/In Press date: 29 July 2018
e-pub ahead of print date: 27 September 2018
Keywords: Area-level model, design MSE, mixed logistic model, model-based MSE

Identifiers

Local EPrints ID: 425560
URI: http://eprints.soton.ac.uk/id/eprint/425560
ISSN: 0306-7734
PURE UUID: e19bacba-1ca5-4daf-bb1f-0e9f866a8919

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Date deposited: 24 Oct 2018 16:30
Last modified: 07 Oct 2020 06:34

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