Nonparametric Bayesian inference for ergodic diffusions

L. Panzar; J.H. Zanten, van

doi:10.1016/j.jspi.2009.06.003

Nonparametric Bayesian inference for ergodic diffusions

L. Panzar, J.H. Zanten, van

Statistics

Research output: Contribution to journal › Article › Academic › peer-review

12 Citations (Scopus)

Abstract

The problem of nonparametric drift estimation for ergodic diffusions is studied from a Bayesian perspective. In particular, Gaussian process priors are exhibited that yield optimal contraction rates if the drift function belongs to a smoothness class.

Original language	English
Pages (from-to)	4204-4210
Journal	Journal of Statistical Planning and Inference
Volume	139
Issue number	12
DOIs	https://doi.org/10.1016/j.jspi.2009.06.003
Publication status	Published - 2009

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title = "Nonparametric Bayesian inference for ergodic diffusions",

abstract = "The problem of nonparametric drift estimation for ergodic diffusions is studied from a Bayesian perspective. In particular, Gaussian process priors are exhibited that yield optimal contraction rates if the drift function belongs to a smoothness class.",

author = "L. Panzar and {Zanten, van}, J.H.",

year = "2009",

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language = "English",

volume = "139",

pages = "4204--4210",

journal = "Journal of Statistical Planning and Inference",

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AU - Panzar, L.

AU - Zanten, van, J.H.

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Y1 - 2009

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AB - The problem of nonparametric drift estimation for ergodic diffusions is studied from a Bayesian perspective. In particular, Gaussian process priors are exhibited that yield optimal contraction rates if the drift function belongs to a smoothness class.

U2 - 10.1016/j.jspi.2009.06.003

DO - 10.1016/j.jspi.2009.06.003

M3 - Article

SN - 0378-3758

VL - 139

SP - 4204

EP - 4210

JO - Journal of Statistical Planning and Inference

JF - Journal of Statistical Planning and Inference

IS - 12

ER -

Nonparametric Bayesian inference for ergodic diffusions

Abstract

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