Modified Maximum Likelihood Estimation in Poisson Regression
Evrim Oral · Biometrics & Biostatistics International Journal · 2017
In Generalized Linear Models, likelihood equations are intractable and do not have explicit solutions; thus, they must be solved by using Newton-type algorithms.Solving these equations by iterations, however, can be problematic: the iterations might converge to wrong values or the iterations might not converge at all.In this study, we derive the modified maximum likelihood estimators for Poisson regression model and study their properties.We also search the robustness of these estimators when there are outliers in the covariates.