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Modelling complex survey data with population level information: an empirical likelihood approach

Oguz Alper, Melike and Berger, Yves G. (2016) Modelling complex survey data with population level information: an empirical likelihood approach Biometrika, 2, (103), pp. 447-459. (doi:10.1093/biomet/asw014).

Record type: Article


Survey data are often collected with unequal probabilities from a stratified population. In many modelling situations, the parameter of interest is a subset of a set of parameters, with the others treated as nuisance parameters. We show that in this situation the empirical likelihood ratio statistic follows a chi-squared distribution asymptotically, under stratified single and multi-stage unequal probability sampling, with negligible sampling fractions. Simulation studies show that the empirical likelihood confidence interval may achieve better coverages and has more balanced tail error rates than standard approaches, which involve variance estimation, linearization or resampling.

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Accepted/In Press date: 17 March 2016
Published date: 23 May 2016
Related URLs:
Keywords: design-based inference, empirical likelihood, estimating equation, inclusion probability, regression parameter, unequal probability sampling
Organisations: Statistical Sciences Research Institute


Local EPrints ID: 376699
ISSN: 0006-3444
PURE UUID: 1fd9d044-9964-42eb-81f1-7edc75cf116c

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Date deposited: 05 May 2015 14:36
Last modified: 18 Jul 2017 04:04

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Author: Melike Oguz Alper
Author: Yves G. Berger

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