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BAYESIAN NONPARAMETRIC PLACKETT-LUCE MODELS FOR THE ANALYSIS OF PREFERENCES FOR COLLEGE DEGREE PROGRAMMES

Authors: 

Francois Caron, Yee Whye Teh, Brendan Murphy

Publication Type: 
Refereed Original Article
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.
Digital Object Identifer (DOI): 
10.1214/14-AOAS717
Publication Status: 
Published
Publication Date: 
01/06/2014
Journal: 
The Annals of Applied Statistics
Institution: 
National University of Ireland, Dublin (UCD)
Open access repository: 
No