Nieto-Barajas, L.E. and Prunster, I. and Walker, S.G. (2004) Normalized random measures driven by increasing additive processes. Annals of Statistics, 32 (6). pp. 2343-2360. ISSN 0090-5364.
|PDF (Normalized Random Measures)|
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.
|Uncontrolled keywords:||Bayesian nonparametric inference; distribution of means of random probability measures; increasing additive process; Levy measure; mixtures of Dirichlet process|
|Subjects:||Q Science > QA Mathematics (inc Computing science)|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics|
|Depositing User:||Judith Broom|
|Date Deposited:||26 Sep 2008 14:58|
|Last Modified:||06 Sep 2011 00:35|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/10537 (The current URI for this page, for reference purposes)|
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