Determining Bayesian sample size and Bayesian optimal model : power estimation under misclassification and variable selection for linear regression model
Language: English
Published by LAP LAMBERT Academic Publishing, 2010
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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nach der Bestellung gedruckt Neuware - Printed after ordering - This dissertation contains three topics using the Bayesian paradigm for statistical inference. The first topic is related to Bayesian sample size determination with a misclassified prevalence variable when two possibly dependent diagnostic tests are used for estimation. After accounting for the dependence structure, the required sample size will be larger than that assuming independence between the tests. The second topic is also concerned with Bayesian sample size calculation with a misclassified binary response variable. Differing from the first topic, an error-free covariate is added. Simulations demonstrate that choices of prior distributions have a great impact on the resultant sample size. The last topic is about Bayesian variable selection under the multiple linear regression model. Two competing Bayesian methods are Bayesian model averaging and reversible jump MCMC. It is found that reversible jump MCMC, expected to give better models, does not seem to differ from Bayesian model averaging in examples considered.…
Seller Inventory # 9783838317731
- Title
- Determining Bayesian sample size and Bayesian optimal model : power estimation under misclassification and variable selection for linear regression model
- Author
- Dunlei Cheng
- Publisher
- LAP LAMBERT Academic Publishing
- Publication year
- 2010
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 3838317734
- ISBN 13
- 9783838317731
- Item weight
- 167 grams
- Dimensions
- 220x150x7 mm
"Synopsis" may belong to another edition of this title.
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