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Computational Statistics

, Volume 18, Issue 1, pp 79–106 | Cite as

Simulated Maximum Likelihood in Nonlinear Continuous-Discrete State Space Models: Importance Sampling by Approximate Smoothing

  • Hermann Singer
Article

Summary

The likelihood function of a continuous-discrete state space model is computed recursively by Monte Carlo integration, using importance sampling techniques. A functional integral representation of the transition density is utilized and importance densities are obtained by smoothing. Examples are the likelihood surfaces of an AR(2) process, a Ginzburg-Landau model and stock price models with stochastic volatilities.

Keywords

Stochastic differential equations Nonlinear filtering Discrete noisy measurements Maximum likelihood estimation Monte Carlo simulation Importance sampling 

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Copyright information

© Physica-Verlag 2003

Authors and Affiliations

  • Hermann Singer
    • 1
  1. 1.Lehrstuhl für angewandte Statistik und Methoden der empirischen SozialforschungFernUniversität HagenHagenGermany

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