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Calibration of predictions in regression
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UNSPECIFIED (2000) Calibration of predictions in regression. COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 29 (9-10). pp. 1973-1986. ISSN 0361-0926.
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Abstract
A regression predictor is well-calibrated if the predictions it gives are equal to the average responses that would be observed in an independent sample. The usual least squares predictor does not have this property, but its calibration can be improved by shrinking the predictions by a factor which depends on the signal-to-noise ratio of the regression model. We suggest a semi-Bayesian approach to estimating this factor, giving an estimate closely related to the so-called Stein Shrinkage Factor. The results are illustrated on a large medical data set.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > QA Mathematics | ||||
Journal or Publication Title: | COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | ||||
Publisher: | MARCEL DEKKER INC | ||||
ISSN: | 0361-0926 | ||||
Official Date: | 2000 | ||||
Dates: |
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Volume: | 29 | ||||
Number: | 9-10 | ||||
Number of Pages: | 14 | ||||
Page Range: | pp. 1973-1986 | ||||
Publication Status: | Published |
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