Mixture models for ranked data classification

Analysis of ranking data is required in several research fields. In the present work we review statistical models for random rankings and propose an original generalization of a popular parametric distribution, that we name extended Plackett-Luce model, to account for the order of the ranking elicitation process. We illustrate the validity of the novel model with its successful maximum likelihood estimation from the real data set of the Large Fragment Phage Display (LFPD) ex- periment, where the epitope mapping of a specific human protein is the main goal. In particular we address the heterogeneous nature of theexperimental units via a finite mixture model approach and compare the performances when alternative ranking models are employed as mixture components