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UBC Theses and Dissertations
Bayesian cross-validation choice and assessment of statistical models Alqallaf, Fatemah Ali
Abstract
This thesis will be concerned with application of a cross-validation criterion to the choice and assessment of statistical models, in which observed data are partitioned, with one part of the data compared to predictions conditional on the model and the rest of the data. We develop three methods, gold, silver, and bronze based on the idea of splitting data in the context of measuring prediction error; however, they can also be adapted for model checking. The gold method uses analytic calculations for the posterior predictive distribution; however, the silver method avoids this mathematical intensity, instead simulating many posterior samples, and the bronze method reduces the amount of sampling to speed up computation. We also consider the Bayesian p-value in which the posterior distribution can be used to check model adequacy, in the context of cross-validation with repeated data splitting. Application to examples is detailed, using the discussed methodologies of estimation and prediction.
Item Metadata
Title |
Bayesian cross-validation choice and assessment of statistical models
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
1999
|
Description |
This thesis will be concerned with application of a cross-validation criterion
to the choice and assessment of statistical models, in which observed data
are partitioned, with one part of the data compared to predictions conditional
on the model and the rest of the data.
We develop three methods, gold, silver, and bronze based on the idea
of splitting data in the context of measuring prediction error; however, they
can also be adapted for model checking. The gold method uses analytic calculations
for the posterior predictive distribution; however, the silver method
avoids this mathematical intensity, instead simulating many posterior samples,
and the bronze method reduces the amount of sampling to speed up
computation.
We also consider the Bayesian p-value in which the posterior distribution
can be used to check model adequacy, in the context of cross-validation
with repeated data splitting. Application to examples is detailed, using the
discussed methodologies of estimation and prediction.
|
Extent |
2983982 bytes
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Genre | |
Type | |
File Format |
application/pdf
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Language |
eng
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Date Available |
2009-06-17
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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 |
10.14288/1.0080055
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
1999-11
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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.