THE PERFORMANCE OF COMPUTER INTENSIVE METHODS FOR OVER-DISPERSED CATEGORICAL DATA(Categorical Data Analysis)

In order to deal with over-dispersed categorical data, several methods have been proposed. Those methods include parametric model extensions, quasi-likelihood methods, generalized estimating equations, as well as nonparametric approaches. In this paper, applications of the computer intensive methods, such as the jackknife method and the bootstrap method, are considered. The methods considered here assume the baseline models to be original ones that are used under the multinomial distribution assumption. Then, for each resampling dataset, the maximum likelihood estimates under the assumption are obtained. The purpose of this paper is to evaluate the performance of the estimators of the regression parameters and their variances and to compare it with other procedures. We review the approaches and show the results of a simulation study.