A comparison of preliminary estimators in a class of ordinal data models

In this paper, we propose several initial values for the EM algorithm of maximum likelihood estimates of the parameters in a class of models, called CUB, recently introduced for ordinal data. Specifically, we compare the algorithmic efficiency of each estimator with respect to a naive proposal through a vast simulation experiment. The results confirm a substantial gain in efficiency of the moments estimators over the whole parametric space. Then, some extensions are also discussed and several applications to real data sets are presented.