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2011 Particle-based likelihood inference in partially observed diffusion processes using generalised Poisson estimators
Jimmy Olsson, Jonas Ströjby
Electron. J. Statist. 5: 1090-1122 (2011). DOI: 10.1214/11-EJS632

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.

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Jimmy Olsson. Jonas Ströjby. "Particle-based likelihood inference in partially observed diffusion processes using generalised Poisson estimators." Electron. J. Statist. 5 1090 - 1122, 2011. https://doi.org/10.1214/11-EJS632

Information

Published: 2011
First available in Project Euclid: 15 September 2011

zbMATH: 1274.62564
MathSciNet: MR2836770
Digital Object Identifier: 10.1214/11-EJS632

Subjects:
Primary: 62M09
Secondary: 65C05

Rights: Copyright © 2011 The Institute of Mathematical Statistics and the Bernoulli Society

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