Modeling non-Gaussian time-varying vector autoregressive processes by particle filtering

DOI10.1007/s11045-009-0081-8zbMath1298.93301OpenAlexW2002216372MaRDI QIDQ468111

Ayşın Ertüzün, Deniz Gençağa, Ercan Engin Kuruoglu

Publication date: 5 November 2014

Published in: Multidimensional Systems and Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11045-009-0081-8

zbMATH Keywords

sequential Monte Carloparticle filteringcross-correlated processesvector autoregressive processes

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Estimation and detection in stochastic control theory (93E10) Stochastic systems in control theory (general) (93E03)

Related Items (2)

An improved Bernoulli particle filter for single target tracking ⋮ Performance evaluation of particle filter resampling techniques for improved estimation of misalignment and trajectory deviation

Uses Software

• ARfit

Cites Work

• Sequential parameter estimation of time-varying non-Gaussian autoregressive processes
• Sequential Monte Carlo Methods in Practice
• Estimation of parameters and eigenmodes of multivariate autoregressive models
• Unnamed Item

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