Abstract Details
Activity Number:
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547
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Type:
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Contributed
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Date/Time:
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Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract #316730
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Title:
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A Variational EM Approach for Fitting Mixed Membership Models with Rank Data
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Author(s):
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Y. Samuel Wang* and Elena Erosheva
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Companies:
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University of Washington and University of Washington
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Keywords:
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Rank Data ;
Variational ;
Mixed Membership Models
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Abstract:
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Rank data occur in a wide variety of contexts including survey results, consumer preferences or athletic competitions. One way to model population heterogeneity in observed rankings is to use mixed membership models that provide a framework for individual level mixing. This framework allows each individual's membership to spread out across multiple sub-populations. Previous work on mixed membership models for rank data utilizes an MCMC approach that assumes equal-sized sub-populations. We propose a variational EM algorithm for fitting mixed membership models for rank data that allows for fast approximate inference and varying sub-population relative frequencies. We derive the variational EM algorithm, illustrate our estimation approach on data from the 1997 Irish presidential election, and compare our results to the previously published MCMC results on the same data set.
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Authors who are presenting talks have a * after their name.
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