Abstract
The aim of this work is to study the problem of prior elicitation for the consensus ranking in the Mallows model with Spearman’s distance, a popular distance-based model for rankings or permutation data. Previous Bayesian inference for such a model has been limited to the use of the uniform prior over the space of permutations. We present a novel strategy to elicit informative prior beliefs on the location parameter of the model, discussing the interpretation of hyper-parameters and the implication of prior choices for the posterior analysis.
Acknowledgments
The authors would like to thank Sonia Petrone, Elja Arjas and Arnoldo Frigessi for their insightful comments. We are also grateful to an anonymous associate editor and two anonymous referees for their helpful comments that greatly improved a previous version of the paper.
The views expressed in this paper are those of the authors and do not necessarily reflect those of the Bank of Italy.
Citation
Marta Crispino. Isadora Antoniano-Villalobos. "Informative Priors for the Consensus Ranking in the Bayesian Mallows Model." Bayesian Anal. 18 (2) 391 - 414, June 2023. https://doi.org/10.1214/22-BA1307
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