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Activity Number:
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607
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #305573 |
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Title:
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Pairwise Likelihood for Binary Data
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Author(s):
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Zi Jin*+ and Nancy Reid
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Companies:
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University of Toronto and University of Toronto
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Address:
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100 St.George Street, Toronto, ON, M5S 3G3, Canada
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Keywords:
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Multivariate logit model ; Pairwise likelihood ; Bahadur representation ; Godambe matrix ; Asymptotic relative efficiency
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Abstract:
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The pairwise likelihood is defined as the product of bivariate density functions for within cluster pairs. Using simulated data based on two different algorithms we explore the performance of the pairwise likelihood method on multivariate binary data. We show that the pairwise likelihood approach outperforms the full likelihood approach, as it provides more accurate estimates, higher efficiency, and is less computationally intensive. Furthermore the pairwise likelihood is more robust to model misspecification.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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