Statistics Seminar: STA 290
Tuesday, October 16th, 2012 at 4.10pm, MSB 1147 (Colloquium Room)
Refreshments 3:30pm, prior to seminar in MSB 4110 (Statistics Lounge)
Speaker: Sanjay Chaudhuri (Dept. Statistics & Applied Probability, University of Singapore)
Title: A Conditional Empirical Likelihood Approach to Combine Sampling Design and Population Level Information
Abstract: Inclusion of available population level information in statistical modeling is known to produce more accurate estimates than those obtained only from the random samples. However, a fully parametric model which incorporates this information 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 registration data with panel survey data to estimate annual first birth probabilities.
This work is joint with Mark Handcock, Department of Statistics, UCLA and Michael Rendall, Department of Sociology, University of Maryland, College Park.