Modeling the Factors Influencing the Evaluations of Items Through Mixture Models for Preference Datasets
In numerous situations, we use ranks dataset to exhibit preferences of a group of respondents towards a set of items. While assigning ranks, judges may consider several factors contributing to overall ranks of items. In this study, an attempt is made to model factors influencing the judges’ evaluations of items through mixture models for preference datasets. Both the probabilistic features of the mixture distribution and inferential as well as computational issues emerging out of the maximum likelihood estimation are addressed. Moreover, empirical evidences from observed dataset confirming the plausibility of the proposed model to preference dataset are provided.