A new method for sequential learning of states and parameters for state-space models: the particle swarm learning optimization

DOI10.1080/00949655.2020.1762600OpenAlexW3024810611MaRDI QIDQ5036844

I. R. E. Guzman, P. C. C. Ccori

Publication date: 23 February 2022

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2020.1762600

zbMATH Keywords

nonlinear modelsparticle filterparticle swarm optimizationswarm computation

Mathematics Subject Classification ID

Bayesian inference (62F15) Robustness and adaptive procedures (parametric inference) (62F35) Statistics (62-XX) Bootstrap, jackknife and other resampling methods (62F40) Stochastic approximation (62L20) Sequential estimation (62L12)

Cites Work

• Unnamed Item
• Unnamed Item
• Stochastic volatility with leverage: fast and efficient likelihood inference
• Stochastic volatility models with leverage and heavy-tailed distributions: a Bayesian approach using scale mixtures
• Bayesian forecasting and dynamic models.
• Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm
• Particle learning and smoothing
• Leverage, heavy-tails and correlated jumps in stochastic volatility models
• Block sampler and posterior mode estimation for asymmetric stochastic volatility models
• On the development of practical nonlinear filters
• Quasi-maximum likelihood estimation of stochastic volatility models
• Stochastic volatility model with leverage and asymmetrically heavy-tailed error using GH skew Student's \(t\)-distribution
• Robust Bayesian analysis of heavy-tailed stochastic volatility models using scale mixtures of normal distributions
• Recursive Bayesian estimation using Gaussian sums
• Bayesian nonparametric modelling of the return distribution with stochastic volatility
• Particle filters and Bayesian inference in financial econometrics
• Marginal Likelihood from the Gibbs Output
• Non-Gaussian State-Space Modeling of Nonstationary Time Series
• Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models
• Multivariate Stochastic Variance Models
• Likelihood analysis of non-Gaussian measurement time series
• Filtering via Simulation: Auxiliary Particle Filters
• The simulation smoother for time series models
• A multi-move sampler for estimating non-Gaussian time series models: Comments on Shephard & Pitt (1997)