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An empirical likelihood approach under cluster sampling with missing observations

An empirical likelihood approach under cluster sampling with missing observations
An empirical likelihood approach under cluster sampling with missing observations
The parameter of interest considered is the unique solution to a set of estimating equations, such as regression parameters of generalised linear models. We consider a design-based approach; that is, the sampling distribution is specified by stratification, cluster (multi-stage) sampling, unequal selection probabilities, side information and a response mechanism. The proposed empirical likelihood approach takes into account of these features. Empirical likelihood has been mostly developed under more restrictive settings, such as independent and identically distributed assumption, which is violated under a design-based framework. A proper empirical likelihood approach which deals with cluster sampling, missing data and multidimensional parameters is absent in the literature. This paper shows that a cluster-level empirical log-likelihood ratio statistic is pivotal. The main contribution of the paper is to provide the rigorous asymptotic theory and underlining regularity conditions which imply $\surd{n}$-consistency and the Wilks’s theorem or self-normalisation property. Negligible and large sampling fractions are considered.
design-based approach, estimating equations, response mechanism, response propensities, stratification, side information, unequal probabilities
0020-3157
Berger, Yves
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Berger, Yves
8fd6af5c-31e6-4130-8b53-90910bf2f43b

Berger, Yves (2018) An empirical likelihood approach under cluster sampling with missing observations. Annals of the Institute of Statistical Mathematics. (In Press)

Record type: Article

Abstract

The parameter of interest considered is the unique solution to a set of estimating equations, such as regression parameters of generalised linear models. We consider a design-based approach; that is, the sampling distribution is specified by stratification, cluster (multi-stage) sampling, unequal selection probabilities, side information and a response mechanism. The proposed empirical likelihood approach takes into account of these features. Empirical likelihood has been mostly developed under more restrictive settings, such as independent and identically distributed assumption, which is violated under a design-based framework. A proper empirical likelihood approach which deals with cluster sampling, missing data and multidimensional parameters is absent in the literature. This paper shows that a cluster-level empirical log-likelihood ratio statistic is pivotal. The main contribution of the paper is to provide the rigorous asymptotic theory and underlining regularity conditions which imply $\surd{n}$-consistency and the Wilks’s theorem or self-normalisation property. Negligible and large sampling fractions are considered.

Text Berger_2018_AISM - Accepted Manuscript
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Accepted/In Press date: 22 June 2018
Keywords: design-based approach, estimating equations, response mechanism, response propensities, stratification, side information, unequal probabilities

Identifiers

Local EPrints ID: 421738
URI: https://eprints.soton.ac.uk/id/eprint/421738
ISSN: 0020-3157
PURE UUID: 2ec88a74-a770-4f30-9249-e2e3f97bf368

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Date deposited: 26 Jun 2018 16:30
Last modified: 28 Jun 2018 16:30

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Contributors

Author: Yves Berger

University divisions

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