Asymptotic properties of particle filter-based maximum likelihood estimators for state space models

DOI10.1016/j.spa.2007.05.007zbMath1132.62064OpenAlexW1989048944MaRDI QIDQ2476295

Tobias Rydén, Jimmy Olsson

Publication date: 18 March 2008

Published in: Stochastic Processes and their Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spa.2007.05.007

zbMATH Keywords

consistencyasymptotic normalityhidden Markov modelmaximum likelihoodstate space modelsparticle filtersequential Monte Carlo methods

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Asymptotic properties of parametric estimators (62F12) Inference from stochastic processes and prediction (62M20) Non-Markovian processes: estimation (62M09) Markov processes: estimation; hidden Markov models (62M05) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (6)

Estimation of agent-based models using sequential Monte Carlo methods ⋮ A Survey of Sequential Monte Carlo Methods for Economics and Finance ⋮ Particle-based likelihood inference in partially observed diffusion processes using generalised Poisson estimators ⋮ Long-term stability of sequential Monte Carlo methods under verifiable conditions ⋮ The time machine: a simulation approach for stochastic trees ⋮ Uniform time average consistency of Monte Carlo particle filters

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