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DC Field | Value | Language |
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dc.contributor.author | Alhamzawi, R | - |
dc.contributor.author | Yu, K | - |
dc.date.accessioned | 2016-01-28T09:38:36Z | - |
dc.date.available | 2015-09-22 | - |
dc.date.available | 2016-01-28T09:38:36Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Journal of Statistical Computation and Simulation, 85 (14): pp. 2903 - 2918, (2014) | en_US |
dc.identifier.issn | 0094-9655 | - |
dc.identifier.issn | 1563-5163 | - |
dc.identifier.uri | http://www.tandfonline.com/doi/full/10.1080/00949655.2014.945449 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/11937 | - |
dc.description.abstract | A 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.extent | 2903 - 2918 | - |
dc.language.iso | en | en_US |
dc.publisher | Taylor & Francis | en_US |
dc.subject | G-prior | en_US |
dc.subject | Gibbs sampler | en_US |
dc.subject | Ridge parameter | en_US |
dc.subject | Tobit quantile regression | en_US |
dc.subject | Variable selection | en_US |
dc.title | Bayesian Tobit quantile regression using-prior distribution with ridge parameter | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1080/00949655.2014.945449 | - |
dc.relation.isPartOf | Journal of Statistical Computation and Simulation | - |
pubs.issue | 14 | - |
pubs.notes | peerreview_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.notes | peerreview_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-status | Published | - |
pubs.publication-status | Published | - |
pubs.volume | 85 | - |
Appears in Collections: | Dept of Mathematics Research Papers |
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Fulltext.pdf | 202.33 kB | Adobe PDF | View/Open |
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