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Abstract Details
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Activity Number:
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319
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Type:
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Invited
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #300059 |
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Title:
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Desiderata for a Predictive Theory of Statistics
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Author(s):
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Bertrand Salem Clarke*+
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Companies:
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University of Miami
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Address:
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Department of Medicine, Miami, FL, 33136,
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Keywords:
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prequentialism ;
complexity ;
variance-bias ;
stability ;
predictor updating
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Abstract:
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We present a unified treatment for how to approach predictive problems. It is based on six `desiderata' which, taken together, are an effort to clarify what criteria a good predictive theory of statistics should satisfy.
The motivation for this work is that there are many contexts where predictive validation is more important than model identification, which may be practically impossible. This is particularly so in fields involving complex or high dimensional data where model selection, or more generally predictor selection, is the main focus.
Several examples of how the desiderata can be applied in practice to identify good predictors and assess their properties are given.
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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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