Empirical likelihood confidence intervals for complex sampling designs


Berger, Y.G. and De La Riva Torres, O. (2015) Empirical likelihood confidence intervals for complex sampling designs. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 78, (2), 319-341. (doi:10.1111/rssb.12115).

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Description/Abstract

We define an empirical likelihood approach which gives consistent design-based confidence intervals which can be calculated without the need of variance estimates, design effects, resampling, joint inclusion probabilities and linearization, even when the point estimator is not linear. It can be used to construct confidence intervals for a large class of sampling designs and estimators which are solutions of estimating equations. It can be used for means, regressions coefficients, quantiles, totals or counts even when the population size is unknown. It can be used with large sampling fractions and naturally includes calibration constraints. It can be viewed as an extension of the empirical likelihood approach to complex survey data. This approach is computationally simpler than the pseudoempirical likelihood and the bootstrap approaches. The simulation study shows that the confidence interval proposed may give better coverages than the confidence intervals based on linearization, bootstrap and pseudoempirical likelihood. Our simulation study shows that, under complex sampling designs, standard confidence intervals based on normality may have poor coverages, because point estimators may not follow a normal sampling distribution and their variance estimators may be biased.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1111/rssb.12115
ISSNs: 1369-7412 (print)
1467-9868 (electronic)
Related URLs:
Keywords: calibration, design-based approach, finite population corrections, h´ajek estimator, horvitz-thompson estimator, regression estimator, stratification, unequal inclusion probabilities
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics
Divisions : University Structure - Pre August 2011 > Southampton Statistical Sciences Research Institute
Faculty of Social and Human Sciences > Social Sciences > Social Statistics & Demography
ePrint ID: 337688
Accepted Date and Publication Date:
Status
March 2016Published
6 April 2015Made publicly available
19 January 2015Accepted
Date Deposited: 01 May 2012 15:25
Last Modified: 08 Sep 2016 11:32
URI: http://eprints.soton.ac.uk/id/eprint/337688

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