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Outlier robust model-assisted small area estimation

Outlier robust model-assisted small area estimation
Outlier robust model-assisted small area estimation
Small area estimation with M‐quantile models was proposed by Chambers and Tzavidis (2006). The key target of this approach to small area estimation is to obtain reliable and outlier robust estimates avoiding at the same time the need for strong parametric assumptions. This approach, however, does not allow for the use of unit level survey weights, making questionable the design consistency of the estimators unless the sampling design is self‐weighting within small areas. In this paper, we adopt a model‐assisted approach and construct design consistent small area estimators that are based on the M‐quantile small area model. Analytic and bootstrap estimators of the design‐based variance are discussed. The proposed estimators are empirically evaluated in the presence of complex sampling designs.
0323-3847
157-175
Fabrizi, E.
5cedd1a8-038a-4325-ac2e-1096cb60c8d2
Salvati, N.
d1b7ebe3-afad-40fb-b32c-e748e344e922
Pratesi, M.
083eb444-b19b-4e6b-bb39-cc182bea9412
Tzavidis, N.
431ec55d-c147-466d-9c65-0f377b0c1f6a
Fabrizi, E.
5cedd1a8-038a-4325-ac2e-1096cb60c8d2
Salvati, N.
d1b7ebe3-afad-40fb-b32c-e748e344e922
Pratesi, M.
083eb444-b19b-4e6b-bb39-cc182bea9412
Tzavidis, N.
431ec55d-c147-466d-9c65-0f377b0c1f6a

Fabrizi, E., Salvati, N., Pratesi, M. and Tzavidis, N. (2014) Outlier robust model-assisted small area estimation. Biometrical Journal, 56 (1), 157-175. (doi:10.1002/bimj.201200095).

Record type: Article

Abstract

Small area estimation with M‐quantile models was proposed by Chambers and Tzavidis (2006). The key target of this approach to small area estimation is to obtain reliable and outlier robust estimates avoiding at the same time the need for strong parametric assumptions. This approach, however, does not allow for the use of unit level survey weights, making questionable the design consistency of the estimators unless the sampling design is self‐weighting within small areas. In this paper, we adopt a model‐assisted approach and construct design consistent small area estimators that are based on the M‐quantile small area model. Analytic and bootstrap estimators of the design‐based variance are discussed. The proposed estimators are empirically evaluated in the presence of complex sampling designs.

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Fabrizi et al 2014 Biometrical Journal - Version of Record
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e-pub ahead of print date: 10 October 2013
Published date: 1 January 2014
Organisations: Social Statistics & Demography, Statistical Sciences Research Institute

Identifiers

Local EPrints ID: 350486
URI: https://eprints.soton.ac.uk/id/eprint/350486
ISSN: 0323-3847
PURE UUID: f6d743aa-ea89-4d6d-b9ab-a2e197078b5d
ORCID for N. Tzavidis: ORCID iD orcid.org/0000-0002-8413-8095

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Date deposited: 27 Mar 2013 12:53
Last modified: 17 Sep 2019 01:01

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