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Exact and computationally efficient likelihood-based estimation for discretely observed diffusion processes (with discussion)
Article first published online: 15 MAY 2006
DOI: 10.1111/j.1467-9868.2006.00552.x
Issue
Journal of the Royal Statistical Society: Series B (Statistical Methodology)
Volume 68, Issue 3, pages 333–382, June 2006
Additional Information
How to Cite
Beskos, A., Papaspiliopoulos, O., Roberts, G. O. and 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: 333–382. doi: 10.1111/j.1467-9868.2006.00552.x
Publication History
- Issue published online: 15 MAY 2006
- Article first published online: 15 MAY 2006
- [Read before The Royal Statistical Society at a meeting organized by the Research Section on Wednesday, October 12th, 2005, Professor T. J. Sweeting in the Chair]
- Abstract
- Article
- References
- Cited By
Keywords:
- Cox–Ingersoll–Ross model;
- EM algorithm;
- Graphical models;
- Markov chain Monte Carlo methods;
- Monte Carlo maximum likelihood;
- Retrospective sampling
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.