Consistent nonparametric Bayesian inference for discretely observed scalar diffusions
Statistics Theory
2013-02-01 v1 Statistics Theory
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.
Cite
@article{arxiv.1301.7567,
title = {Consistent nonparametric Bayesian inference for discretely observed scalar diffusions},
author = {Frank van der Meulen and Harry van Zanten},
journal= {arXiv preprint arXiv:1301.7567},
year = {2013}
}
Comments
Published in at http://dx.doi.org/10.3150/11-BEJ385 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)