5 papers
Efficient and accurate inference for mixtures of Mallows models with Spearman distance
Marta Crispino, Cristina Mollica, Valerio Astuti +1
The Mallows model occupies a central role in parametric modelling of ranking data to learn preferences of a population of judges. Despite the wide range of metrics for rankings tha…
New algorithms and goodness-of-fit diagnostics from remarkable properties of ranking models
Cristina Mollica, Luca Tardella
The forward order assumption postulates that the ranking process of the items is carried out by sequentially assigning the positions from the top (most-liked) to the bottom (least-…
Bayesian analysis of ranking data with the constrained Extended Plackett-Luce model
Cristina Mollica, Luca Tardella
Multistage ranking models, including the popular Plackett-Luce distribution (PL), rely on the assumption that the ranking process is performed sequentially, by assigning the positi…
Moment-based Bayesian Poisson Mixtures for inferring unobserved units
Danilo Alunni Fegatelli, Luca Tardella
We exploit a suitable moment-based characterization of the mixture of Poisson distribution for developing Bayesian inference for the unknown size of a finite population whose units…
Algorithms and diagnostics for the analysis of preference rankings with the Extended Plackett-Luce model
Cristina Mollica, Luca Tardella
Choice behavior and preferences typically involve numerous and subjective aspects that are difficult to be identified and quantified. For this reason, their exploration is frequent…