Parameters Estimation In Generalized Linear Models

Generalized linear models have unified the approach to regression for a wide variety of continuous and discrete data. The latter in particular has a very important significance in practical application. Therefore the study of the generalized linear model is very meaningful, then the parameters estimation in the study of such problems has been a focal point of concern to everyone. Parameters estimation of the model was crucial aspect of statistical inference. Extensions of generalized linear models to include random effects has, thus far, been hampered by the need for numerical integration to evaluate likelihoods. Based on previous researches this article give a summary of some parameters estimation algorithms in generalized linear models, and evaluated. Particularly, those in generalized linear mixed models. Lastly, comparing these methods and make a reasonable assessment through simulations.