Abstract Details
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Activity Number:
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538
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Type:
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Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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| Abstract - #309812 |
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Title:
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Model-Based Clustering of Gaussian Regression Time Series
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Author(s):
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Semhar Michael*+ and Volodymyr Melnykov
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Companies:
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The University of Alabama and The University of Alabama
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Keywords:
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model-based clustering ;
finite mixture model ;
regression time series
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Abstract:
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A novel estimation procedure for mixture models of Gaussian regression time series is developed. We show that the Bayesian information criterion can be used to choose the optimal number of mixture components and correctly assess the order of the model. The performance of our method is evaluated via a simulation study. The results are promising as the proposed approach overcomes the limitations of other methods developed so far.
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Authors who are presenting talks have a * after their name.
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