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Abstract Details
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Activity Number:
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490
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Type:
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Invited
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Date/Time:
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Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Survey Research Methods
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| Abstract - #300286 |
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Title:
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Calibrated Bayes Inference for Sample Surveys
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Author(s):
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Rod Little*+
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Companies:
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University of Michigan
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Address:
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1415 Washington Heights , Ann Arbor, MI, 48109, USA
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Keywords:
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Calibrated Bayes ;
survey sampling ;
design based inference
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Abstract:
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The calibrated Bayesian approach to surveys basis inferences on the posterior predictive distribution from a Bayesian model, but seeks inferences that are frequency calibrated, in the sense of have good frequentist properties in repeated sampling. The calibrated aspect dictates that survey design features like clustering and weighting need to be appropriately included in the model, to protect against model misspecification. Examples are provided on how to do this, and calibrated Bayes inferences are shown by simulation to have better frequentist properties than standard design based approaches, including the model-assisted approach popular with current survey practitioners.
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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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