A grade of membership model for rank data

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Title: A grade of membership model for rank data
Authors: Gormley, Isobel Claire
Murphy, Thomas Brendan
Permanent link: http://hdl.handle.net/10197/7121
Date: Jun-2009
Online since: 2015-09-25T15:38:06Z
Abstract: A grade of membership (GoM) model is an individual level mixture model which allows individuals have partial membership of the groups that characterize a population. A GoM model for rank data is developed to model the particular case when the response data is ranked in nature. A Metropolis-withinGibbs sampler provides the framework for model fitting, but the intricate nature of the rank data models makes the selection of suitable proposal distributions difficult. 'Surrogate' proposal distributions are constructed using ideas from optimization transfer algorithms. Model fitting issues such as label switching and model selection are also addressed. The GoM model for rank data is illustrated through an analysis of Irish election data where voters rank some or all of the candidates in order of preference. Interest lies in highlighting distinct groups of voters with similar preferences (i.e. 'voting blocs') within the electorate, taking into account the rank nature of the response data, and in examining individuals’ voting bloc memberships. The GoM model for rank data is fitted to data from an opinion poll conducted during the Irish presidential election campaign in 1997.
Funding Details: Irish Research Council for Science, Engineering and Technology
Science Foundation Ireland
Type of material: Journal Article
Publisher: International Society for Bayesian Analysis (ISBA)
Journal: Bayesian Analysis
Volume: 4
Issue: 2
Start page: 265
End page: 295
Copyright (published version): 2009 International Society for Bayesian Analysis
Keywords: Grade of membership modelsPlackett-Luce modelSurrogate proposal distributionsRank dataVoting blocs
DOI: 10.1214/09-BA410
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection

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