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Activity Number:
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306
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Type:
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Luncheons
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Date/Time:
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Tuesday, August 8, 2006 : 12:30 PM to 1:50 PM
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Sponsor:
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Section on Survey Research Methods
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| Abstract - #306424 |
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Title:
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Bayesian Inference for Complex Sample Surveys
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Author(s):
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Roderick J. Little*+
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Companies:
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University of Michigan
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Address:
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Department of Biostatistics, Ann Arbor, MI, 48109,
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Keywords:
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Bayes ; superpopulation models ; predictive inference
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Abstract:
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Finite population sampling is perhaps the only area of statistics where the primary mode of analysis is based on the randomization distribution, rather than on statistical models for the measured variables. The Bayesian modeling approach to survey inference often is rejected as being too subjective, but the approach can be used successfully in the large survey setting by formulating weak models that take into account the survey design and by including relatively noninformative priors. This roundtable will promote a dialogue about the strengths and weaknesses of this approach relative to design-based inference.
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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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