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Generalized linear models incorporating population level information: an empirical-likelihood-based approach

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Abstract:

Summary. 

In many situations information from a sample of individuals can be supplemented by population level information on the relationship between a dependent variable and explanatory variables. Inclusion of the population level information can reduce bias and increase the efficiency of the parameter estimates. Population level information can be incorporated via constraints on functions of the model parameters. In general the constraints are non-linear, making the task of maximum likelihood estimation more difficult. We develop an alternative approach exploiting the notion of an empirical likelihood. It is shown that, within the framework of generalized linear models, the population level information corresponds to linear constraints, which are comparatively easy to handle. We provide a two-step algorithm that produces parameter estimates by using only unconstrained estimation. We also provide computable expressions for the standard errors. We give an application to demographic hazard modelling by combining panel survey data with birth registration data to estimate annual birth probabilities by parity.

Keywords: Constrained optimization; Empirical likelihood; Generalized linear models

Document Type: Research Article

DOI: http://dx.doi.org/10.1111/j.1467-9868.2007.00637.x

Affiliations: 1: National University of Singapore, Singapore 2: University of Washington, Seattle, USA 3: RAND Corporation, Santa Monica, USA

Publication date: April 1, 2008

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