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Determining Bayesian sample size and Bayesian optimal model: power estimation under misclassification and variable selection for linear regression model - Softcover
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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.
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Determining Bayesian sample size and Bayesian optimal model
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LAP LAMBERT Academic Publishing Dez 2009, 2009
ISBN 10: 3838317734
ISBN 13: 9783838317731
New
Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 100 pp. Englisch. Seller Inventory # 9783838317731
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Determining Bayesian sample size and Bayesian optimal model
Published by
LAP Lambert Academic Publishing, 2009
ISBN 10: 3838317734
ISBN 13: 9783838317731
New
Softcover
Seller: moluna, Greven, Germany
Seller rating 4 out of 5 stars
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 fo. Seller Inventory # 5412450
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Determining Bayesian sample size and Bayesian optimal model
Published by
LAP LAMBERT Academic Publishing Dez 2009, 2009
ISBN 10: 3838317734
ISBN 13: 9783838317731
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 100 pp. Englisch. Seller Inventory # 9783838317731
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Determining Bayesian sample size and Bayesian optimal model : power estimation under misclassification and variable selection for linear regression model
Published by
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838317734
ISBN 13: 9783838317731
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. 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
Quantity: 1 available