Normalized random measures driven by increasing additive processes
Luis E. Nieto-Barajas, Igor Prünster, and Stephen G. Walker
Source: Ann. Statist. Volume 32, Number 6
(2004), 2343-2360.
Abstract
This 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.
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Keywords: Bayesian nonparametric inference; distribution of means of random probability measures; increasing additive process; Lévy measure; mixtures of Dirichlet process
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Permanent link to this document: http://projecteuclid.org/euclid.aos/1107794871
Digital Object Identifier: doi:10.1214/009053604000000625
Zentralblatt MATH identifier: 1069.62029
Mathematical Reviews number (MathSciNet): MR2153987
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