Generalised regression estimation given imperfectly matched auxiliary data
Generalised regression estimation given imperfectly matched auxiliary data
Generalised regression estimation allows one to make use of available auxiliary information in survey sampling. We develop three types of generalised regression estimator when the auxiliary data cannot be matched perfectly to the sample units, so that the standard estimator is inapplicable. The inference remains design-based. Consistency of the proposed estimators is either given by construction or else can be tested given the observed sample and links. Mean square errors can be estimated. A simulation study is used to explore the potentials of the proposed estimators
incidence weights, record linkage, reverse incidence weights
239 - 255
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
13 March 2021
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
Zhang, Li-Chun
(2021)
Generalised regression estimation given imperfectly matched auxiliary data.
Journal of Official Statistics, 37 (1), .
(doi:10.2478/jos-2021-0010).
Abstract
Generalised regression estimation allows one to make use of available auxiliary information in survey sampling. We develop three types of generalised regression estimator when the auxiliary data cannot be matched perfectly to the sample units, so that the standard estimator is inapplicable. The inference remains design-based. Consistency of the proposed estimators is either given by construction or else can be tested given the observed sample and links. Mean square errors can be estimated. A simulation study is used to explore the potentials of the proposed estimators
Text
ambiguousAux-rev
- Accepted Manuscript
More information
Accepted/In Press date: 25 November 2020
e-pub ahead of print date: 13 March 2021
Published date: 13 March 2021
Additional Information:
Publisher Copyright:
© Li-Chun Zhang, published by Sciendo 2020.
Keywords:
incidence weights, record linkage, reverse incidence weights
Identifiers
Local EPrints ID: 445530
URI: http://eprints.soton.ac.uk/id/eprint/445530
ISSN: 0282-423X
PURE UUID: ba98b01d-a2e0-466c-814b-bfcb77a1508d
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Date deposited: 14 Dec 2020 17:32
Last modified: 17 Mar 2024 03:30
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