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Small sample improvement over Bayes prediction under model uncertainty Wong, Hubert
Abstract
Existing criteria for evaluating the adequacy of a predictive model are model-based (e.g. AIC, BIC, MSPE) or empirical (e.g. PRESS and other cross-validation type criteria). We introduce a new class of "mongrel" criteria for on-line prediction that evaluates candidate predictors based on both model information and past empirical performance. Simulation results showed that the mongrel procedure produced more accurate predictions than the standard Bayes procedure for small sample sizes. This improvement was observed over a wide range of data-generators for the problem of variable selection in normal linear models.
Item Metadata
Title |
Small sample improvement over Bayes prediction under model uncertainty
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2000
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Description |
Existing criteria for evaluating the adequacy of a predictive model are
model-based (e.g. AIC, BIC, MSPE) or empirical (e.g. PRESS and other
cross-validation type criteria). We introduce a new class of "mongrel" criteria
for on-line prediction that evaluates candidate predictors based on both model
information and past empirical performance. Simulation results showed that
the mongrel procedure produced more accurate predictions than the standard
Bayes procedure for small sample sizes. This improvement was observed over
a wide range of data-generators for the problem of variable selection in normal
linear models.
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Extent |
4743544 bytes
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Genre | |
Type | |
File Format |
application/pdf
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Language |
eng
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Date Available |
2009-07-23
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Provider |
Vancouver : University of British Columbia Library
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Rights |
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
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DOI |
10.14288/1.0089709
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2000-11
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Campus | |
Scholarly Level |
Graduate
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Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.