Normalized random measures driven by increasing additive processes
Statistics Theory
2007-06-13 v1 Statistics Theory
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.
Cite
@article{arxiv.math/0508592,
title = {Normalized random measures driven by increasing additive processes},
author = {Luis E. Nieto-Barajas and Igor Prunster and Stephen G. Walker},
journal= {arXiv preprint arXiv:math/0508592},
year = {2007}
}
Comments
Published 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)