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Abstract Details
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Activity Number:
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581
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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ENAR
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| Abstract - #301192 |
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Title:
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Empirical Likelihood-Based Change Point Detection Approach
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Author(s):
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Yijie Xue*+ and Nicole Lazar
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Companies:
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University of Georgia and University of Georgia
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Address:
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Department of Statistics, Athens, GA, 30602,
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Keywords:
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Empirical likelihood ;
Change point detection
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Abstract:
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Change point detection problems are often encountered in quality control and climatological data. The proposed empirical likelihood based approach provides an alternative method to solve this problem. Based on the empirical likelihood distribution with the weights estimated from empirical likelihood ratio, we set a threshold to identify potential change points. Because the empirical likelihood distribution with the estimated weights is asymptotically unbiased for the true distribution, researchers do not need to specify the model of the data, that is, it is model-free. The proposed method will be compared to existing methods via simulations.
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