Inclusion of available population level information in statistical modelling is known to produce more accurate estimates than those obtained only from the random samples. However, a fully parametric model which incorporates both these informations may be computationally challenging to handle. Empirical likelihood based methods can be used to combine these two kinds of information and estimate the model parameters in a computationally efficient way. In this article we consider methods to include sampling weights in an empirical likelihood based estimation procedure to augment population level information in sample-based statistical modeling. Our estimator uses conditional weights and is able to incorporate covariate information both through the weights and the usual estimating equations. We show that under usual assumptions, with population size increasing unbounded, the estimates are strongly consistent, asymptotically unbiased and normally distributed. Moreover, they are more efficient than other probability weighted analogues. Our framework provides additional justification for inverse probability weighted score estimators in terms of conditional empirical likelihood. We give an application to demographic hazard modeling by combining birth regitration data with panel survey data to estimate annual first birth probabilities. This work is joint with Mark Handcock, Department of Statistics, University of California, Los Angeles,USA and Michael Rendall, Department of Sociology, University of Maryland, College Park, USA.
Keywords and Phrases: Empirical likelihood; Complex surveys; Sampling design; Population level information.