Abstract Details
Activity Number:
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686
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Type:
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Contributed
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Date/Time:
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Thursday, August 13, 2015 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #316199
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Title:
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Understanding Variability Between Groups of Sequences Using a Bayesian Object-Oriented Data Model
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Author(s):
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Maria Tackett* and Dan Spitzner
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Companies:
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University of Virginia and University of Virginia
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Keywords:
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Bayesian Methods ;
Life course data ;
object-oriented data ;
Optimal Matching ;
Sequence analysis ;
Social Sciences
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Abstract:
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Optimal Matching is an algorithm used to analyze object-oriented data, specifically sequences. In this article, we propose a framework inspired by classical Analysis of Variance to estimate the variability between groups of sequences using Optimal Matching. To estimate between group vari- ability, we explore approaches based on established methods- bootstrapping and Hidden Markov Models. We also propose a Bayesian object-oriented model. These three methods are examined and demonstrated on a well-known dataset of historical English dance sequences, with a mind towards application to life course data.
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Authors who are presenting talks have a * after their name.
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