Finite sample performance of the E-M algorithm for ranks data modelling

Abstract

We check the finite sample performance of the maximum likelihood estimators of the parameters of a mixture distribution recently introduced for modelling ranks/preference data. The estimates are derived by the E-M algorithm and the performance is evaluated both from an univariate and bivariate points of view. While the results are generally acceptable as far as it concerns the bias, the Monte Carlo experiment shows a different behaviour of the estimators efficiency for the two parameters of the mixture, mainly depending upon their location in the admissible parametric space. Some operative suggestions conclude the paer.