Estimation of parameters and eigenmodes of multivariate autoregressive models
ACM Transactions on Mathematical Software (TOMS)
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Sequential parameter estimation of time-varying non-Gaussian autoregressive processes
EURASIP Journal on Applied Signal Processing
Modeling of non-stationary autoregressive alpha-stable processes by particle filters
Digital Signal Processing
New Introduction to Multiple Time Series Analysis
New Introduction to Multiple Time Series Analysis
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Blind separation of non-stationary sources using continuous density hidden Markov models
Digital Signal Processing
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We present a novel and general methodology for modeling time-varying vector autoregressive processes which are widely used in many areas such as modeling of chemical processes, mobile communication channels and biomedical signals. In the literature, most work utilize multivariate Gaussian models for the mentioned applications, mainly due to the lack of efficient analytical tools for modeling with non-Gaussian distributions. In this paper, we propose a particle filtering approach which can model non-Gaussian autoregressive processes having cross-correlations among them. Moreover, time-varying parameters of the process can be modeled as the most general case by using this sequential Bayesian estimation method. Simulation results justify the performance of the proposed technique, which potentially can model also Gaussian processes as a sub-case.