Journal article  Open Access

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

Gencaga D., Kuruoglu E. E., Ertuzun A.

Vector autoregressive processes  Computer Science Applications  Artificial Intelligence  Information Systems  Sequential Monte Carlo  Particle filtering  Hardware and Architecture  Software  Applied Mathematics  Signal Processing 

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.

Source: Multidimensional systems and signal processing 21 (2010): 73–85. doi:10.1007/s11045-009-0081-8

Publisher: Kluwer Academic Publishers, Dordrecht ;, Stati Uniti d'America


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BibTeX entry
	title = {Modeling non-Gaussian time-varying vector autoregressive processes by particle filtering},
	author = {Gencaga D. and Kuruoglu E.  E. and Ertuzun A.},
	publisher = {Kluwer Academic Publishers, Dordrecht ;, Stati Uniti d'America},
	doi = {10.1007/s11045-009-0081-8},
	journal = {Multidimensional systems and signal processing},
	volume = {21},
	pages = {73–85},
	year = {2010}