JSM 2005 - Toronto

Abstract #304008

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 451
Type: Contributed
Date/Time: Wednesday, August 10, 2005 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract - #304008
Title: A Two-step Empirical Likelihood Approach for Combining Sample and Population Data in Regression Estimation
Author(s): Sanjay Chaudhuri*+ and Mark S. Handcock and Michael S. Rendall
Companies: University of Washington and University of Washington and RAND Corporation
Address: B313 Padelford Hall, Box 354322, Seattle, WA, 98195, United States
Keywords: empirical likelihood ; constrained optimisation ; generalised linear models ; population information
Abstract:

Apart from the sample, sometimes information of the bivariate or multivariate relationship of explanatory variables with the dependent variable may be known from population-level data. Using the method of constrained maximum likelihood estimation, it is possible to include such population-level information and achieve a large reduction in the bias and variance of the estimates of these regression coefficients (Handcock, Rendall, and Cheadle 2004). In this talk, we will discuss an alternative two-step, empirical likelihood-based approach. We first compute optimal weights for the sample, which both maximize the empirical likelihood and satisfy the population constraints. These weights are then used in a standard statistical package to produce the parameter and standard-error estimates. Like the constrained MLE, the use of population constraint leads to correct and substantially lower standard errors. However, the two-step approach is computationally much more flexible, allowing for estimation with multiple population constraints and multiple covariates.

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