PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Gencaga D., Kuruoglu E. E., Ertuzun A. Modeling non-Gaussian time-varying vector autoregressive processes by particle filtering. In: Multidimensional Systems and Signal Processing, vol. 21 (1) pp. 73 - 85. Springer, 2010.
 
 
Abstract
(English)
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.
URL: http://www.springerlink.com/content/v65547v41q51g21k/
DOI: 10.1007/s11045-009-0081-8
Subject Vector autoregressive processes
Sequential Monte Carlo
Particle filtering
Cross-correlated processes
G.3 Probability and Statistics. Stochastic processes
G.3 Probability and Statistics. Probabilistic algorithms (including Monte Carlo)
G.3 Probability and Statistics. Correlation and regression analysis
62L12 Sequential estimation
65C05 Monte Carlo methods
60H35 Computational methods for stochastic equations


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