PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Huang R., Zheng H., Kuruoglu E. E. Time-varying ARMA stable process estimation using sequential Monte Carlo. In: Signal, Image and Video Processing, vol. 7 (5) pp. 951 - 958. Springer, 2013.
 
 
Abstract
(English)
Various time series data in applications ranging from telecommunications to financial analysis and from geophysical signals to biological signals exhibit non-stationary and non-Gaussian characteristics. α-Stable distributions have been popular models for data with impulsive and nonsymmetric characteristics. In this work, we present timevarying autoregressive moving-average α-stable processes as a potential model for a wide range of data, and we propose a method for tracking the time-varying parameters of the processwith α-stable distribution. The technique is based on sequential Monte Carlo, which has assumed a wide popularity in various applications where the data or the system is non-stationary and non-Gaussian.
URL: http://link.springer.com/content/pdf/10.1007%2Fs11760-011-0285-x.pdf
DOI: 10.1007/s11760-011-0285-x
Subject Time varying autoregressive moving average (TVARMA) process
Sequential Monte Carlo
Particle filtering
Alpha-stable process
G.3 PROBABILITY AND STATISTICS. Probabilistic algorithms (including Monte Carlo)
G.3 PROBABILITY AND STATISTICS. Stochastic processes
G.3 PROBABILITY AND STATISTICS. Time series analysis
60G52 Stable processes
62L12 Sequential estimation
62M10 Time series, auto-correlation, regression, etc.
65C05 Monte Carlo methods


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