Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Zhaozhi Qian, Philip L. H. Yu
Title: Weighted Distance-Based Models for Ranking Data Using the R Package rankdist
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.

Page views:: 2134. Submitted: 2015-10-14. Published: 2019-07-31.
Paper: Weighted Distance-Based Models for Ranking Data Using the R Package rankdist     Download PDF (Downloads: 747)
rankdist_1.1.4.tar.gz: R source package Download (Downloads: 24; 28KB)
v90i05.R: R replication code Download (Downloads: 32; 15KB)
helping_functions.R: Supplementary R code Download (Downloads: 30; 3KB)
CogSurvey.RData: Supplementary data (R binary format) Download (Downloads: 27; 13KB)

DOI: 10.18637/jss.v090.i05

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.