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Abstract Details
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Activity Number:
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530
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Type:
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Roundtables
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Date/Time:
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Wednesday, August 3, 2011 : 12:30 PM to 1:50 PM
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Sponsor:
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Section on Health Policy Statistics
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| Abstract - #301220 |
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Title:
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Calibrated Bayes, Models, and the Role of Randomization in Surveys and Experiments
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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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statistical inference ;
likelihood principle ;
randomization
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Abstract:
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The calibrated Bayes approach to statistical inference is Bayesian for the inference, but seeks models that yield inferences that are calibrated, in the sense of having good repeated-sampling properties. The Bayesian approach to inference was historically regarded as not supporting randomization for sample selection or treatment allocation, since the randomization distribution is not the basis for inference. I discuss why randomization is important to me as a calibrated Bayesian, and provide some supporting examples
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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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