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

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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.

Keywords: Cox–Ingersoll–Ross model; EM algorithm; Graphical models; Markov chain Monte Carlo methods; Monte Carlo maximum likelihood; Retrospective sampling

Document Type: Research Article


Affiliations: Lancaster University, UK

Publication date: 2006-06-01

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