• Media type: E-Article
  • Title: Moment Conditions and Bayesian Non-Parametrics
  • Contributor: Bornn, Luke; Shephard, Neil; Solgi, Reza
  • imprint: Oxford University Press (OUP), 2019
  • Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology
  • Language: English
  • DOI: 10.1111/rssb.12294
  • ISSN: 1369-7412; 1467-9868
  • Origination:
  • Footnote:
  • Description: <jats:title>Summary</jats:title><jats:p>Models phrased through moment conditions are central to much of modern inference. Here these moment conditions are embedded within a non-parametric Bayesian set-up. Handling such a model is not probabilistically straightforward as the posterior has support on a manifold. We solve the relevant issues, building new probability and computational tools by using Hausdorff measures to analyse them on real and simulated data. These new methods, which involve simulating on a manifold, can be applied widely, including providing Bayesian analysis of quasi-likelihoods, linear and non-linear regression, missing data and hierarchical models.</jats:p>