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Exact and computationally efficient likelihood-based estimation for discretely observed diffusion processes (with discussion)

Beskos, A; Papaspiliopoulos, O; Roberts, GO; Fearnhead, P; (2006) Exact and computationally efficient likelihood-based estimation for discretely observed diffusion processes (with discussion). Journal of the Royal Statistical Society series B-statistical methodology , 68 pp. 333-382. 10.1111/j.1467-9868.2006.00552.x. Green open access

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Abstract

Summary:The objective of the paper is to present a novel methodology for likelihood‐based inference for discretely observed diffusions. We propose Monte Carlo methods, which build on recent advances on the exact simulation of diffusions, for performing maximum likelihood and Bayesian estimation.

Type: Article
Title: Exact and computationally efficient likelihood-based estimation for discretely observed diffusion processes (with discussion)
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/j.1467-9868.2006.00552.x
Publisher version: https://doi.org/10.1111/j.1467-9868.2006.00552.x
Language: English
Additional information: This version is the author accepted manuscrip. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Cox–Ingersoll–Ross model, EM algorithm, Graphical models, Markov chain Monte Carlo methods, Monte Carlo maximum likelihood, Retrospective sampling
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10050412
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