Consistent nonparametric Bayesian inference for discretely observed scalar diffusions

F.H. Meulen, van der; J.H. Zanten, van

doi:10.3150/11-BEJ385

Consistent nonparametric Bayesian inference for discretely observed scalar diffusions

F.H. Meulen, van der, J.H. Zanten, van

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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. Keywords: Bayesian nonparametrics; drift function; posterior consistency; posterior distribution; stochastic differential equations; wavelets

Original language	English
Pages (from-to)	44-63
Number of pages	20
Journal	Bernoulli
Volume	19
Issue number	1
DOIs	https://doi.org/10.3150/11-BEJ385
Publication status	Published - 2013

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title = "Consistent nonparametric Bayesian inference for discretely observed scalar diffusions",

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. Keywords: Bayesian nonparametrics; drift function; posterior consistency; posterior distribution; stochastic differential equations; wavelets",

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AB - 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. Keywords: Bayesian nonparametrics; drift function; posterior consistency; posterior distribution; stochastic differential equations; wavelets

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DO - 10.3150/11-BEJ385

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JO - Bernoulli

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Consistent nonparametric Bayesian inference for discretely observed scalar diffusions

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