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Activity Number:
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566
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #304766 |
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Title:
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Entropy-Based Empirical Likelihood Ratio Change Point Detection Policies
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Author(s):
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Albert Vexler*+ and Gregory Gurevich
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Companies:
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New York State University at Buffalo and Sami Shamoon College
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Address:
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Department of Biostatistics, Buffalo, NY, 14214,
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Keywords:
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Empirical likelihood ; Sample entropy ; Change point ; CUSUM ; Shiryayev-Roberts procedure ; Nonparametric tests
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Abstract:
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Nonparametric detection of a change in distribution is studied when observations are independent. We provide a general method of constructing distribution-free change point detection schemes that have approximately likelihood structures. This method utilizes principles of the maximum empirical likelihood (EL) approach based on sample entropy. Entropy-based tests have been well addressed in the literature in the context of powerful decision rules for goodness-of-fit. We extend the entropy-based technique, using the EL principles, to be applied to change point detection policies. CUSUM and Shiryayev-Roberts (SR) detection policies are shown to be powerful parametric likelihood tests for detecting a change in distribution. We apply the proposed method to obtain nonparametric forms of the CUSUM and SR procedures.
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