Nonparametric Bayesian modelling using skewed Dirichlet processes

dc.creatorIglesias, Pilar L.
dc.creatorOrellana, Yasna
dc.creatorQuintana, Fernando A.
dc.date.accessioned2024-01-10T12:07:01Z
dc.date.accessioned2024-05-02T18:26:25Z
dc.date.available2024-01-10T12:07:01Z
dc.date.available2024-05-02T18:26:25Z
dc.date.created2024-01-10T12:07:01Z
dc.date.issued2009
dc.identifier10.1016/j.jspi.2008.07.009
dc.identifier1873-1171
dc.identifier0378-3758
dc.identifierhttps://doi.org/10.1016/j.jspi.2008.07.009
dc.identifierhttps://repositorio.uc.cl/handle/11534/76234
dc.identifierWOS:000262061300042
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9270342
dc.description.abstractWe introduce a new class of discrete random probability measures that extend the definition of Dirichlet process (DP) by explicitly incorporating skewness. The asymmetry is controlled by a single parameter in such a way that symmetric DPs are obtained as a special case of the general construction. We review the main properties of skewed DPs and develop appropriate Polya urn schemes. We illustrate the modelling in the context of linear regression models of the capital asset pricing model (CAPM) type, where assessing symmetry for the error distribution is important to check validity of the model. (C) 2008 Elsevier B.V. All rights reserved.
dc.languageen
dc.publisherELSEVIER
dc.rightsacceso restringido
dc.subjectBayes factor
dc.subjectDensity estimation
dc.subjectDirichlet process
dc.subjectLinear regression model
dc.subjectPolya sequence
dc.subjectSkewed distribution
dc.subjectGENERAL-CLASS
dc.subjectREGRESSION
dc.subjectDISTRIBUTIONS
dc.subjectINFERENCE
dc.subjectMIXTURE
dc.subjectDENSITY
dc.titleNonparametric Bayesian modelling using skewed Dirichlet processes
dc.typeartículo


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