Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/12504
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dc.contributor.authorAlhamzawi, R-
dc.contributor.authorYu, K-
dc.date.accessioned2016-04-18T09:28:09Z-
dc.date.available2014-02-07-
dc.date.available2016-04-18T09:28:09Z-
dc.date.issued2014-
dc.identifier.citationJournal of Statistical Computation and Simulation, 84(4), pp. 868 - 880, (2014)en_US
dc.identifier.issn0094-9655-
dc.identifier.issn1563-5163-
dc.identifier.urihttp://www.tandfonline.com/doi/abs/10.1080/00949655.2012.731689-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/12504-
dc.description.abstractIn this paper, we discuss the regularization in linear-mixed quantile regression. A hierarchical Bayesian model is used to shrink the fixed and random effects towards the common population values by introducing an l1 penalty in the mixed quantile regression check function. A Gibbs sampler is developed to simulate the parameters from the posterior distributions. Through simulation studies and analysis of an age-related macular degeneration (ARMD) data, we assess the performance of the proposed method. The simulation studies and the ARMD data analysis indicate that the proposed method performs well in comparison with the other approaches. © 2012 Taylor & Francis.en_US
dc.format.extent868 - 880-
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.subjectAsymmetric Laplace distributionen_US
dc.subjectGibbs sampleren_US
dc.subjectRandom effectsen_US
dc.subjectLongitudinal dataen_US
dc.subjectQuantile regressionen_US
dc.titleBayesian Lasso-mixed quantile regressionen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1080/00949655.2012.731689-
dc.relation.isPartOfJournal of Statistical Computation and Simulation-
pubs.issue4-
pubs.publication-statusPublished-
pubs.publication-statusPublished-
pubs.volume84-
Appears in Collections:Dept of Mathematics Research Papers

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