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Empirical likelihood approach for aligning information from multiple surveys

Empirical likelihood approach for aligning information from multiple surveys
Empirical likelihood approach for aligning information from multiple surveys
When two surveys carried out separately in the same population have common variables, it might be desirable to adjust each survey’s weights so that they give equal estimates for the common variables. This problem has been studied extensively and has often been referred to as ‘alignment’ or ‘numerical consistency’. We develop a design-based empirical likelihood approach for alignment and estimation of complex parameters defined by estimating equations. We focus on a general case when a single set of adjusted weights, which can be applied to both common and non-common variables, is produced for each survey. The main contribution of the paper is to show that the empirical log-likelihood ratio statistic is pivotal in presence of alignment constraints. This pivotal statistic can be used to test hypotheses and derive confidence regions. Hence, the empirical likelihood approach proposed for alignment possesses the self-normalization property, under a design-based approach. The proposed approach accommodates large sampling fractions, stratification and population level auxiliary information. It is particularly well suited for inference about small domains, when data are skewed. It includes implicit adjustments when the samples considerably differ in size. The confidence regions are constructed without the need for variance estimates, joint-inclusion probabilities, linearisation and re-sampling.
Design-based approach, design-consistency, estimating equations, inclusion probabilities, population-level information, stratification
0306-7734
Berger, Yves
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Kabzinska, Ewa
3d907e04-e7e7-4059-9c3c-9938be5fd4c4
Berger, Yves
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Kabzinska, Ewa
3d907e04-e7e7-4059-9c3c-9938be5fd4c4

Berger, Yves and Kabzinska, Ewa (2019) Empirical likelihood approach for aligning information from multiple surveys. International Statistical Review. (doi:10.1111/insr.12337).

Record type: Article

Abstract

When two surveys carried out separately in the same population have common variables, it might be desirable to adjust each survey’s weights so that they give equal estimates for the common variables. This problem has been studied extensively and has often been referred to as ‘alignment’ or ‘numerical consistency’. We develop a design-based empirical likelihood approach for alignment and estimation of complex parameters defined by estimating equations. We focus on a general case when a single set of adjusted weights, which can be applied to both common and non-common variables, is produced for each survey. The main contribution of the paper is to show that the empirical log-likelihood ratio statistic is pivotal in presence of alignment constraints. This pivotal statistic can be used to test hypotheses and derive confidence regions. Hence, the empirical likelihood approach proposed for alignment possesses the self-normalization property, under a design-based approach. The proposed approach accommodates large sampling fractions, stratification and population level auxiliary information. It is particularly well suited for inference about small domains, when data are skewed. It includes implicit adjustments when the samples considerably differ in size. The confidence regions are constructed without the need for variance estimates, joint-inclusion probabilities, linearisation and re-sampling.

Text
Berger Kabzinska 2019 - Accepted Manuscript
Restricted to Repository staff only until 30 April 2020.
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More information

Accepted/In Press date: 1 May 2019
e-pub ahead of print date: 2 September 2019
Keywords: Design-based approach, design-consistency, estimating equations, inclusion probabilities, population-level information, stratification

Identifiers

Local EPrints ID: 430761
URI: https://eprints.soton.ac.uk/id/eprint/430761
ISSN: 0306-7734
PURE UUID: 818530ba-ff60-4012-92c2-18c5b8c9e008
ORCID for Yves Berger: ORCID iD orcid.org/0000-0002-9128-5384

Catalogue record

Date deposited: 10 May 2019 16:30
Last modified: 18 Oct 2019 00:37

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