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dc.contributor.authorPadellini, Tullia
dc.contributor.authorRue, Haavard
dc.date.accessioned2018-04-19T10:45:31Z
dc.date.available2018-04-19T10:45:31Z
dc.date.issued2018-04-10
dc.identifier.urihttp://hdl.handle.net/10754/627566
dc.description.abstractQuantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite the fact that this leads to a proper posterior for the regression coefficients, the resulting posterior variance is however affected by an unidentifiable parameter, hence any inferential procedure beside point estimation is unreliable. We propose a model-based approach for quantile regression that considers quantiles of the generating distribution directly, and thus allows for a proper uncertainty quantification. We then create a link between quantile regression and generalised linear models by mapping the quantiles to the parameter of the response variable, and we exploit it to fit the model with R-INLA. We extend it also in the case of discrete responses, where there is no 1-to-1 relationship between quantiles and distribution's parameter, by introducing continuous generalisations of the most common discrete variables (Poisson, Binomial and Negative Binomial) to be exploited in the fitting.
dc.publisherarXiv
dc.relation.urlhttp://arxiv.org/abs/1804.03714v1
dc.relation.urlhttp://arxiv.org/pdf/1804.03714v1
dc.rightsArchived with thanks to arXiv
dc.titleModel-based Quantile Regression for Discrete Data
dc.typePreprint
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.eprint.versionPre-print
dc.contributor.institutionDipartimento di Scienze Statistiche, Sapienza Universita di Roma
dc.identifier.arxivid1804.03714
kaust.personRue, Haavard
refterms.dateFOA2018-06-14T05:55:11Z


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