Abstract
In this paper, we consider analysis of categorical data that may plausibly contain over/under dispersion. We focus our attention to covariate effects estimation and evaluation. Several methods, such as maximum likelihood approach based on multinomial distribution and its parametric extension, i.e. Dirichlet-multinomial (DM) distribution and quasi-likelihood methods or generalized estimating equations with use of partial structure of the DM distribution, as well as computer intensive methods, such as Jackknife methods and Bootstrap methods are reviewed. Their relative advantages and limits are examined and discussed via their applications to some actual data sets.
