Determining Bayesian sample size and Bayesian optimal model: power estimation under misclassification and variable selection for linear regression model - Couverture souple

Cheng, Dunlei

 
9783838317731: Determining Bayesian sample size and Bayesian optimal model: power estimation under misclassification and variable selection for linear regression model
Couverture souple
ISBN 10 : 3838317734 ISBN 13 : 9783838317731
Editeur : LAP Lambert Academic Publishing, 2010

Synopsis

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.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

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.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP Lambert Academic Publishing
Date d'édition
2010
Langue
anglais
ISBN 10 
3838317734
ISBN 13 
9783838317731
Reliure
Broché
Nombre de pages
100
Coordonnées du fabricant
non disponible
Personne responsable
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