A mixture of experts model for rank data with applications in election studies

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Title: A mixture of experts model for rank data with applications in election studies
Authors: Gormley, Isobel Claire
Murphy, Thomas Brendan
Permanent link: http://hdl.handle.net/10197/7105
Date: Dec-2008
Abstract: A voting bloc is defined to be a group of voters who have similar voting preferences. The cleavage of the Irish electorate into voting blocs is of interest. Irish elections employ a 'single transferable vote' electoral system; under this system voters rank some or all of the electoral candidates in order of preference. These rank votes provide a rich source of preference information from which inferences about the composition of the electorate may be drawn. Additionally, the influence of social factors or covariates on the electorate composition is of interest. A mixture of experts model is a mixture model in which the model parameters are functions of covariates. A mixture of experts model for rank data is developed to provide a model-based method to cluster Irish voters into voting blocs, to examine the influence of social factors on this clustering and to examine the characteristic preferences of the voting blocs. The Benter model for rank data is employed as the family of component densities within the mixture of experts model; generalized linear model theory is employed to model the influence of covariates on the mixing proportions. Model fitting is achieved via a hybrid of the EM and MM algorithms. An example of the methodology is illustrated by examining an Irish presidential election. The existence of voting blocs in the electorate is established and it is determined that age and government satisfaction levels are important factors in influencing voting in this election.
Funding Details: Irish Research Council for Science, Engineering and Technology
Science Foundation Ireland
Type of material: Journal Article
Publisher: Institute of Mathematical Statistics
Copyright (published version): 2008 Institute of Mathematical Statistics
Keywords: Rank dataMixture modelsGeneralized linear modelsEM algorithmMM algorithm
DOI: 10.1214/08-AOAS178
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection

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