Weighted distance-based models for ranking data using the R package rankdist

Zhaozhi QIAN, Leung Ho Philip YU

Research output: Contribution to journalArticles

2 Citations (Scopus)

Abstract

Rankdist is a recently developed R package which implements various distance-based ranking models. These models capture the occurring probability of rankings based on the distances between them. The package provides a framework for fitting and evaluating finite mixture of distance-based models. This paper also presents a new probability model for ranking data based on a new notion of weighted Kendall distance. The new model is flexible and more interpretable than the existing models. We show that the new model has an analytic form of the probability mass function and the maximum likelihood estimates of the model parameters can be obtained efficiently even for ranking involving a large number of objects. Copyright © 2019 American Statistical Association. All rights reserved.
Original languageEnglish
JournalJournal of Statistical Software
Volume90
Issue number5
DOIs
Publication statusPublished - Jul 2019

Citation

Qian, Z., & Yu, P. L. H. (2019). Weighted distance-based models for ranking data using the R package rankdist. Journal of Statistical Software, 90(5). Retrieved from https://doi.org/10.18637/jss.v090.i05

Keywords

  • Ranking data
  • Distance-based models
  • Kendall distance
  • Mixtures models
  • Rank aggregation
  • R.

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