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Preferences in college applications - a nonparametric Bayesian analysis of top-10 rankings
Date Issued
2010-12-10
Date Available
2011-03-10T10:19:16Z
Abstract
Applicants to degree courses in Irish colleges and universities rank up to ten degree courses from a list of over five hundred. These data provide a wealth of
information concerning applicant degree choices. A Dirichlet process mixture of
generalized Mallows models are used to explore data from a cohort of applicants.
We find strong and diverse clusters, which in turn gains us important insights into
the workings of the system. No previously tried models or analysis technique are
able to model the data with comparable accuracy.
information concerning applicant degree choices. A Dirichlet process mixture of
generalized Mallows models are used to explore data from a cohort of applicants.
We find strong and diverse clusters, which in turn gains us important insights into
the workings of the system. No previously tried models or analysis technique are
able to model the data with comparable accuracy.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Subjects
Subject – LCSH
Cluster analysis
College applications--Mathematical models
College choice--Mathematical models
Bayesian statistical decision theory
Language
English
Status of Item
Peer reviewed
Conference Details
NIPS Workshop on Computational Social Science and the Wisdom of Crowds, December 10th 2010, Whistler, Canada
This item is made available under a Creative Commons License
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ali.pdf
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74.6 KB
Format
Adobe PDF
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