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Activity Number:
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226
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Type:
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Invited
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Date/Time:
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Tuesday, July 31, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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| Abstract - #307950 |
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Title:
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Predicting Future Responses Based on Possibly Mis-specified Working Models
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Author(s):
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Lu Tian*+ and Tianxi Cai and Scott Solomon and Lee-Jen Wei
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Companies:
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Northwestern University and Harvard University and Brigham and Women's Hospital and Harvard University
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Address:
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Dept. of Preventive Medicine, Chicago, IL, 60611,
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Keywords:
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K-fold cross validation ; Model misspecification ; optimal prediction region
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Abstract:
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Under a general regression setting, we propose an optimal unconditional prediction procedure for future responses. The resulting prediction intervals or regions have a desirable average coverage level over a set of covariate vectors of interest. When the working model is not correctly specified, the traditional conditional prediction method is generally invalid. On the other hand, one can empirically calibrate the above unconditional procedure and also obtain its cross-validated counterpart. Various large and small sample properties of these unconditional methods are examined analytically and numerically. We find that the K-fold cross validated procedure performs exceptionally well even for cases with rather small sample sizes. The new proposals are illustrated with a real example.
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