Normalized random measures driven by increasing additive processes

dc.creatorNieto-Barajas, Luis E.
dc.creatorPrunster, Igor
dc.creatorWalker, Stephen G.
dc.date2005-08-30
dc.date.accessioned2026-07-07T08:07:14Z
dc.date.available2026-07-07T08:07:14Z
dc.descriptionThis paper introduces and studies a new class of nonparametric prior distributions. Random probability distribution functions are constructed via normalization of random measures driven by increasing additive processes. In particular, we present results for the distribution of means under both prior and posterior conditions and, via the use of strategic latent variables, undertake a full Bayesian analysis. Our class of priors includes the well-known and widely used mixture of a Dirichlet process.
dc.descriptionPublished at http://dx.doi.org/10.1214/009053604000000625 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/math/0508592
dc.identifierhttp://arxiv.org/abs/math/0508592
dc.identifierAnnals of Statistics 2004, Vol. 32, No. 6, 2343-2360
dc.identifierdoi:10.1214/009053604000000625
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/130873
dc.subjectStatistics Theory
dc.subject62F15 (Primary) 60G57. (Secondary)
dc.titleNormalized random measures driven by increasing additive processes
dc.typetext

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