Particle-based likelihood inference in partially observed diffusion processes using generalised Poisson estimators

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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
  • Jimmy Olsson
  • Jonas Ströjby
Enheter & grupper
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Sannolikhetsteori och statistik

Nyckelord

Originalspråkengelska
Sidor (från-till)1090-1122
TidskriftElectronic Journal of Statistics
Volym5
StatusPublished - 2011
PublikationskategoriForskning
Peer review utfördJa