Bayesian Linear Regression with Conditional Heteroskedasticity

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dc.contributor.other Universidad Carlos III de Madrid. Departamento de Estadística 2015-04-17T12:36:19Z 2015-04-17T12:36:19Z 2015-04-01
dc.identifier.issn 2387-0303
dc.description.abstract In this paper we consider adaptive Bayesian semiparametric analysis of the linear regression model in the presence of conditional heteroskedasticity. The distribution of the error term on predictors are modelled by a normal distribution with covariate-dependent variance. We show that a rate-adaptive procedure for all smoothness levels of this standard deviation function is performed if the prior is properly chosen. More specifically, we derive adaptive posterior distribution rate up to a logarithm factor for the conditional standard deviation based on a transformation of hierarchical Gaussian spline prior and log-spline prior respectively.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries 15-04
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.subject.other Bayesian linear regression
dc.subject.other Conditional heteroskedasticity
dc.subject.other Rate of convergence
dc.subject.other Posterior distribution
dc.subject.other Adaptation
dc.subject.other Hierarchical Gaussian spline prior
dc.subject.other Log-spline prior
dc.title Bayesian Linear Regression with Conditional Heteroskedasticity
dc.type workingPaper
dc.rights.accessRights openAccess
dc.type.version submitedVersion
dc.identifier.uxxi DT/0000001355
dc.identifier.repec ws1504
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