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Activity Number:
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55
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 2, 2009 : 4:00 PM to 5:50 PM
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Sponsor:
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Social Statistics Section
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| Abstract - #304606 |
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Title:
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Adaptive Hierarchical Bayes Estimation of Small-Area Proportions
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Author(s):
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Benmei Liu*+
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Companies:
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University of Maryland
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Address:
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Joint Program in Survey Methodology, College Park, MD, 20742,
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Keywords:
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Proportions ; random effects ; exponential power distribution ; ; ;
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Abstract:
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Unit level logistic regression models with mixed effects are sometimes used for estimating small-area proportions, with normality commonly being assumed for the random effects. However, in practical applications the random effects often exhibit significant departures from normality. To reduce the risk of model misspecification, we propose an hierarchical Bayes estimation approach in which the distribution of the random effects is chosen adaptively from the exponential power class of probability distributions. The richness of the exponential power class ensures the robustness of this hierarchical Bayes approach against departure from normality. We demonstrate the robustness of our proposed model using both simulated data and real data.
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