Simulated maximum likelihood in nonlinear continuous-discrete state space models: importance sampling by approximate smoothing

DOI10.1007/s001800300133zbMath1037.62097OpenAlexW2547546409MaRDI QIDQ1424631

Hermann Singer

Publication date: 16 March 2004

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s001800300133

zbMATH Keywords

stochastic differential equationsMonte Carlo simulationnonlinear filteringdiscrete noisy measurements

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Monte Carlo methods (65C05) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (3)

Importance sampling for Kolmogorov backward equations ⋮ Fast continuous-discrete DAF-filters ⋮ Moment equations and Hermite expansion for nonlinear stochastic differential equations with application to stock price models

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