The Ornstein-Uhlenbeck Dirichlet Process and other time-varying processes for Bayesian nonparametric inference

Griffin, Jim E. (2011) The Ornstein-Uhlenbeck Dirichlet Process and other time-varying processes for Bayesian nonparametric inference. Journal of Statistical Planning and Inference, 141 (11). pp. 3648-3664. ISSN 0378-3758. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Official URL
http://dx.doi.org/10.1016/j.jspi.2011.05.019

Abstract

This paper introduces a new class of time-varying, measure-valued stochastic processes for Bayesian nonparametric inference. The class of priors is constructed by normalising a stochastic process derived from non-Gaussian Ornstein-Uhlenbeck processes and generalises the class of normalised random measures with independent increments from static problems. Some properties of the normalised measure are investigated. A particle filter and MCMC schemes are described for inference. The methods are applied to an example in the modelling of financial data.

Item Type: Article
Uncontrolled keywords: Normalised random measures with independent increments; Ornstein–Uhlenbeck process; Time-dependent Bayesian nonparametrics; Particle filtering; Dirichlet process
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jim Griffin
Date Deposited: 30 May 2012 10:05
Last Modified: 21 May 2014 11:23
Resource URI: https://kar.kent.ac.uk/id/eprint/29592 (The current URI for this page, for reference purposes)
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