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Unconditional empirical likelihood approach for analytic use of public survey data: Empirical likelihood for public surveys

Unconditional empirical likelihood approach for analytic use of public survey data: Empirical likelihood for public surveys
Unconditional empirical likelihood approach for analytic use of public survey data: Empirical likelihood for public surveys

Modeling survey data often requires having the knowledge of design and weighting variables. With public-use survey data, some of these variables may not be available for confidentiality reasons. The proposed approach can be used in this situation, as long as calibrated weights and variables specifying the strata and primary sampling units are available. It gives consistent point estimation and a pivotal statistics for testing and confidence intervals. The proposed approach does not rely on with-replacement sampling, single-stage, negligible sampling fractions, or noninformative sampling. Adjustments based on design effects, eigenvalues, joint-inclusion probabilities or bootstrap, are not needed. The inclusion probabilities and auxiliary variables do not have to be known. Multistage designs with unequal selection of primary sampling units are considered. Nonresponse can be easily accommodated if the calibrated weights include reweighting adjustment for nonresponse. We use an unconditional approach, where the variables and sample are random variables. The design can be informative.

calibration, estimating equation, informative sampling, multistage sampling, unequal inclusion probability, weights
0303-6898
Berger, Yves
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Berger, Yves
8fd6af5c-31e6-4130-8b53-90910bf2f43b

Berger, Yves (2022) Unconditional empirical likelihood approach for analytic use of public survey data: Empirical likelihood for public surveys. Scandinavian Journal of Statistics. (doi:10.1111/sjos.12590).

Record type: Article

Abstract

Modeling survey data often requires having the knowledge of design and weighting variables. With public-use survey data, some of these variables may not be available for confidentiality reasons. The proposed approach can be used in this situation, as long as calibrated weights and variables specifying the strata and primary sampling units are available. It gives consistent point estimation and a pivotal statistics for testing and confidence intervals. The proposed approach does not rely on with-replacement sampling, single-stage, negligible sampling fractions, or noninformative sampling. Adjustments based on design effects, eigenvalues, joint-inclusion probabilities or bootstrap, are not needed. The inclusion probabilities and auxiliary variables do not have to be known. Multistage designs with unequal selection of primary sampling units are considered. Nonresponse can be easily accommodated if the calibrated weights include reweighting adjustment for nonresponse. We use an unconditional approach, where the variables and sample are random variables. The design can be informative.

Text
Scandinavian J Statistics - 2022 - Berger - Unconditional empirical likelihood approach for analytic use of public survey - Accepted Manuscript
Restricted to Repository staff only until 13 March 2023.
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Text
Paper_CJS_Oct_2021 - Accepted Manuscript
Restricted to Repository staff only until 13 March 2023.
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More information

Accepted/In Press date: 13 March 2022
e-pub ahead of print date: 18 April 2022
Additional Information: Publisher Copyright: © 2022 The Author. Scandinavian Journal of Statistics published by John Wiley & Sons Ltd on behalf of The Board of the Foundation of the Scandinavian Journal of Statistics.
Related URLs:
Keywords: calibration, estimating equation, informative sampling, multistage sampling, unequal inclusion probability, weights

Identifiers

Local EPrints ID: 456395
URI: http://eprints.soton.ac.uk/id/eprint/456395
ISSN: 0303-6898
PURE UUID: 5400495e-6727-4628-8d55-f9ded8d21807
ORCID for Yves Berger: ORCID iD orcid.org/0000-0002-9128-5384

Catalogue record

Date deposited: 27 Apr 2022 15:45
Last modified: 13 Aug 2022 01:37

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