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

Beskos Alexandros | Zendy; Papaspiliopoulos Omiros | Zendy; Roberts Gareth O. | Zendy; Fearnhead Paul | Zendy

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

Author(s) -

Beskos Alexandros,

Papaspiliopoulos Omiros,

Roberts Gareth O.,

Fearnhead Paul

Publication year - 2006

Publication title -

journal of the royal statistical society: series b (statistical methodology)

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 6.523

H-Index - 137

eISSN - 1467-9868

pISSN - 1369-7412

DOI - 10.1111/j.1467-9868.2006.00552.x

Subject(s) - inference , maximum likelihood , computer science , monte carlo method , bayesian probability , diffusion , maximum likelihood sequence estimation , markov chain monte carlo , bayesian inference , likelihood function , estimation , estimation theory , marginal likelihood , algorithm , mathematics , econometrics , statistical physics , statistics , artificial intelligence , engineering , physics , thermodynamics , systems engineering

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.

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