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We consider an empirical likelihood framework for inference for a statisticalmodel based on an informative sampling design. Covariate information isincorporated both through the weights and the estimating equations. Theestimator is based on conditional weights. We show that under usual conditions,with population size increasing unbounded, the estimates are stronglyconsistent, asymptotically unbiased and normally distributed. Our frameworkprovides additional justification for inverse probability weighted scoreestimators in terms of conditional empirical likelihood. In doing so, itbridges the gap between design-based and model-based modes of inference insurvey sampling settings. We illustrate these ideas with an application to anelectoral survey.

A Conditional Empirical Likelihood Based Method for Model Parameter Estimation from Complex survey Datasets