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Biometrical J 1996;38(1):81-96

Fitting Mixed Poisson Regression Models Using Quasi-Likelihood Methods

Chen JJ, Ahn H

Abstract

This paper is to investigate the use of the quasi-likelihood, extended quasi-likelihood, and pseudo-likelihood approach to estimating and testing the mean parameters with respect to two variance models, M1: ¿ ¿¿(1+¿phis;) and M2: ¿ ¿¿(1+¿). Simulation was conducted to compare the bias and standard deviation, and type I error of the Wald tests, based on the model-based and robust variance estimates, using the three semi-parametric approaches under four mixed Poisson models, two variance structures, and two sample sizes. All methods perform reasonably well in terms of bias. Type I error of the Wald test, based on either the model-based or robust estimate, tends to be larger than the nominal level when over-dispersion is moderate. The extended quasi-likelihood method with the variance model M1 performs more consistently in terms of the efficiency and controlling the type I error than with the model M2, and better than the pseudo-likelihood approach with either the M1 or M2 model. The model-based estimate seems to perform better than the robust estimate when the sample size is small.


Category: Journal Article
DOI: 10.1002/bimj.4710380108
Includes FDA Authors from Scientific Area(s): Toxicological Research
Entry Created: 2012-12-25 Entry Last Modified: 2013-02-26
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