Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/11937
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dc.contributor.authorAlhamzawi, R-
dc.contributor.authorYu, K-
dc.date.accessioned2016-01-28T09:38:36Z-
dc.date.available2015-09-22-
dc.date.available2016-01-28T09:38:36Z-
dc.date.issued2015-
dc.identifier.citationJournal of Statistical Computation and Simulation, 85 (14): pp. 2903 - 2918, (2014)en_US
dc.identifier.issn0094-9655-
dc.identifier.issn1563-5163-
dc.identifier.urihttp://www.tandfonline.com/doi/full/10.1080/00949655.2014.945449-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/11937-
dc.description.abstractA Bayesian approach is proposed for coefficient estimation in the Tobit quantile regression model. The proposed approach is based on placing a g-prior distribution depends on the quantile level on the regression coefficients. The prior is generalized by introducing a ridge parameter to address important challenges that may arise with censored data, such as multicollinearity and overfitting problems. Then, a stochastic search variable selection approach is proposed for Tobit quantile regression model based on g-prior. An expression for the hyperparameter g is proposed to calibrate the modified g-prior with a ridge parameter to the corresponding g-prior. Some possible extensions of the proposed approach are discussed, including the continuous and binary responses in quantile regression. The methods are illustrated using several simulation studies and a microarray study. The simulation studies and the microarray study indicate that the proposed approach performs well.en_US
dc.format.extent2903 - 2918-
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.subjectG-prioren_US
dc.subjectGibbs sampleren_US
dc.subjectRidge parameteren_US
dc.subjectTobit quantile regressionen_US
dc.subjectVariable selectionen_US
dc.titleBayesian Tobit quantile regression using-prior distribution with ridge parameteren_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1080/00949655.2014.945449-
dc.relation.isPartOfJournal of Statistical Computation and Simulation-
pubs.issue14-
pubs.notespeerreview_statement: The publishing and review policy for this title is described in its Aims & Scope. aims_and_scope_url: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=gscs20-
pubs.notespeerreview_statement: The publishing and review policy for this title is described in its Aims & Scope. aims_and_scope_url: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=gscs20-
pubs.publication-statusPublished-
pubs.publication-statusPublished-
pubs.volume85-
Appears in Collections:Dept of Mathematics Research Papers

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