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Bayesian Nonparametric Plackett-Luce Models for the Analysis of Preferences for College Degree Programmes

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Author(s)
Caron, François 
Whye Teh, Yee 
Murphy, Thomas Brendan 
Uri
http://hdl.handle.net/10197/8216
Date Issued
2014
Date Available
14T11:14:28Z December 2016
Abstract
In this paper we propose a Bayesian nonparametric model for clustering partial ranking data.We start by developing a Bayesian nonparametric extension of the popular Plackett-Luce choice model that can handle an infinite number of choice items. Our framework is based on the theory of random atomic measures, with prior specified by a completely random measure. We characterise the posterior distribution given data, and derive a simple and effective Gibbs sampler for posterior simulation. We then develop a Dirichlet process mixture extension of our model and apply it to investigate the clustering of preferences for college degree programmes amongst Irish secondary school graduates. The existence of clusters of applicants who have similar preferences for degree programmes is established and we determine that subject matter and geographical location of the third level institution characterise these clusters.
Sponsorship
European Commission
Type of Material
Journal Article
Publisher
Institute of Mathematical Statistics
Journal
Annals of Applied Statistics
Volume
8
Issue
2
Start Page
1145
End Page
1181
Keywords
  • Machine learning

  • Statistics

  • Ranking data

  • Permutations

  • Gamma process

  • Dirichlet process

  • Mixture models

DOI
10.1214/14-AOAS717
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Owning collection
Insight Research Collection
Scopus© citations
31
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Jan 29, 2023
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