Consistent nonparametric Bayesian inference for discretely observed scalar diffusions

Open Access
Authors
Publication date 02-2013
Journal Bernoulli
Volume | Issue number 19 | 1
Pages (from-to) 44-63
Organisations
  • Faculty of Science (FNWI) - Korteweg-de Vries Institute for Mathematics (KdVI)
Abstract We study Bayes procedures for the problem of nonparametric drift estimation for one-dimensional, ergodic diffusion models from discrete-time, low-frequency data. We give conditions for posterior consistency and verify these conditions for concrete priors, including priors based on wavelet expansions.
Document type Article
Language English
Published at https://doi.org/10.3150/11-BEJ385
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Meulen_vZanten_Bernouilli_2013.pdf (Final published version)
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