Open Access
December 2004 Normalized random measures driven by increasing additive processes
Luis E. Nieto-Barajas, Igor Prünster, Stephen G. Walker
Ann. Statist. 32(6): 2343-2360 (December 2004). DOI: 10.1214/009053604000000625


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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Luis E. Nieto-Barajas. Igor Prünster. Stephen G. Walker. "Normalized random measures driven by increasing additive processes." Ann. Statist. 32 (6) 2343 - 2360, December 2004.


Published: December 2004
First available in Project Euclid: 7 February 2005

zbMATH: 1069.62029
MathSciNet: MR2153987
Digital Object Identifier: 10.1214/009053604000000625

Primary: 62F15
Secondary: 60G57

Keywords: Bayesian nonparametric inference , distribution of means of random probability measures , increasing additive process , Lévy measure , mixtures of Dirichlet process

Rights: Copyright © 2004 Institute of Mathematical Statistics

Vol.32 • No. 6 • December 2004
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