• Medientyp: E-Artikel
  • Titel: Moment Conditions and Bayesian Non-Parametrics
  • Beteiligte: Bornn, Luke; Shephard, Neil; Solgi, Reza
  • Erschienen: Oxford University Press (OUP), 2019
  • Erschienen in: Journal of the Royal Statistical Society Series B: Statistical Methodology
  • Sprache: Englisch
  • DOI: 10.1111/rssb.12294
  • ISSN: 1369-7412; 1467-9868
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  • Beschreibung: <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>