Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/11039
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dc.contributor.authorHashem, H-
dc.contributor.authorVinciotti, V-
dc.contributor.authorAlhamzawi, R-
dc.contributor.authorYu, K-
dc.date.accessioned2015-06-22T13:00:42Z-
dc.date.available2015-04-17-
dc.date.available2015-06-22T13:00:42Z-
dc.date.issued2015-
dc.identifier.citationAdvances in Data Analysis and Classification, 2015en_US
dc.identifier.issn1862-5347-
dc.identifier.issn1862-5355-
dc.identifier.urihttp://link.springer.com/article/10.1007%2Fs11634-015-0206-x-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/11039-
dc.description.abstractApplications of regression models for binary response are very common and models specific to these problems are widely used. Quantile regression for binary response data has recently attracted attention and regularized quantile regression methods have been proposed for high dimensional problems. When the predictors have a natural group structure, such as in the case of categorical predictors converted into dummy variables, then a group lasso penalty is used in regularized methods. In this paper, we present a Bayesian Gibbs sampling procedure to estimate the parameters of a quantile regression model under a group lasso penalty for classification problems with a binary response. Simulated and real data show a good performance of the proposed method in comparison to mean-based approaches and to quantile-based approaches which do not exploit the group structure of the predictors.en_US
dc.languageeng-
dc.language.isoenen_US
dc.publisherSpringer Berlin Heidelbergen_US
dc.subjectBinary regressionen_US
dc.subjectGibbs samplingen_US
dc.subjectQuantile regressionen_US
dc.subjectRegularized regressionen_US
dc.subject62H12en_US
dc.subject62F15en_US
dc.titleQuantile regression with group lasso for classificationen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1007/s11634-015-0206-x-
dc.relation.isPartOfAdvances in Data Analysis and Classification-
pubs.publication-statusAccepted-
pubs.publication-statusAccepted-
Appears in Collections:Dept of Mathematics Research Papers

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