Particle-based likelihood inference in partially observed diffusion processes using generalised Poisson estimators
Forskningsoutput: Tidskriftsbidrag › Artikel i vetenskaplig tidskrift
Abstract
This paper concerns the use of the expectation-maximisation (EM) algorithm for inference in partially observed diffusion processes. In this context, a well known problem is that all except a few diffusion processes lack closed-form expressions of the transition densities. Thus, in order to estimate efficiently the EM intermediate quantity we construct, using novel techniques for unbiased estimation of diffusion transition densities, a random weight fixed-lag auxiliary particle smoother, which avoids the well known problem of particle trajectory degeneracy in the smoothing mode. The estimator is justified theoretically and demonstrated on a simulated example.
Detaljer
Författare | |
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Enheter & grupper | |
Forskningsområden | Ämnesklassifikation (UKÄ) – OBLIGATORISK
Nyckelord |
Originalspråk | engelska |
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Sidor (från-till) | 1090-1122 |
Tidskrift | Electronic Journal of Statistics |
Volym | 5 |
Status | Published - 2011 |
Publikationskategori | Forskning |
Peer review utförd | Ja |