Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 565894, 15 pages
http://dx.doi.org/10.1155/2012/565894
Research Article

A Bayesian Analysis of Spectral ARMA Model

1DEST, FCT, Unesp, Presidente Prudente 19060-900, SP, Brazil
2DECOM, FEEC, Unicamp, Campinas 13083-852, SP, Brazil

Received 22 November 2011; Revised 9 April 2012; Accepted 12 April 2012

Academic Editor: Kwok W. Wong

Copyright © 2012 Manoel I. Silvestre Bezerra et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis, Prentice Hall, Englewood Cliffs, NJ, USA, 3rd edition, 1994, Forecasting and Control. View at Zentralblatt MATH
  2. S. L. Marple, Jr., Digital Spectral Analysis with Applications, Prentice Hall Signal Processing Series, Prentice Hall, Englewood Cliffs, NJ, USA, 1987.
  3. S. M. Kay, Modern Spectral Estimation Theory and Applications, Prentice-Hall, Englewood Cliffs, NJ, USA, 1988.
  4. C. W. Therrien, Discrete Random Signals and Statistical Signal Processing, Prentice-Hall, Englewood Cliffs, NJ, USA, 1992.
  5. J. A. Cadzow, “High performance spectral estimation—a new ARMA method,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 28, no. 5, pp. 524–529, 1980. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  6. B. Friedlander and B. Porat, “The modified Yule-Walker method of ARMA spectral estimation,” IEEE Transactions on Aerospace and Electronic Systems, vol. 20, no. 2, pp. 158–173, 1984. View at Publisher · View at Google Scholar
  7. M. I. S. Bezerra, “Precision of Yule-Walker methods for the ARMA spectral model,” in Proceedings of the IASTED Conference on Circuits, Signals, and Systems, pp. 54–59, Clearwater Beach, Fla, USA, 2004.
  8. M. I. S. Bezerra, Y. Iano, and M. H. Tarumoto, “Evaluating some Yule-Walker methods with the maximum-likelihood estimator for the spectral ARMA model,” Tendências em Matemática Aplicada e Computacional, vol. 9, no. 2, pp. 175–184, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  9. B. L. Jackson, “Frequency-domain Steiglitz-McBride method for least-squares IIR filter design, ARMA modeling, and periodogram smoothing,” IEEE Signal Processing Letters, vol. 15, pp. 49–52, 2008. View at Google Scholar
  10. H. Jeffreys, Theory of Probability, Oxford University Press, London, UK, 3rd edition, 1967.
  11. J. M. Marriot, N. M. Spencer, and A. N. Pettit, “Bayesian approach to selecting covariates for prediction,” Tech. Rep., Trent University, 1996. View at Google Scholar
  12. W. K. Hastings, “Monte Carlo sampling methods using Markov chains and their applications,” Biometrika, vol. 57, pp. 97–109, 1970. View at Google Scholar
  13. N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. Teller, and H. Teller, “Equations of state calculations by fast computing machines,” Journal of Chemical Physics, vol. 21, pp. 1087–1091, 1953. View at Google Scholar
  14. A. F. M. Smith and G. O. Roberts, “Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods,” Journal of the Royal Statistical Society Series B, vol. 55, no. 1, pp. 3–23, 1993. View at Google Scholar · View at Zentralblatt MATH
  15. A. E. Gelfand and A. F. M. Smith, “Sampling-based approaches to calculating marginal densities,” Journal of the American Statistical Association, vol. 85, no. 410, pp. 398–409, 1990. View at Google Scholar · View at Zentralblatt MATH
  16. W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, Markov Chain Monte Carlo in Practice, Chapman & Hall, London, UK, 1996.
  17. D. N. Politis, “Computer intensive methods in statistical analysis,” IEEE Signal Processing Magazine, vol. 15, pp. 39–55, 1998. View at Google Scholar
  18. A. S. W. Batista, Métodos de estimação dos parâmetros dos modelos ARMA para análise espectral [Mastership in Electrical Engineering], FEE, UNICAMP, 1992.
  19. R. L. Moses, V. Simonyte, P. Stoica, and T. Söderström, “An efficient linear method for ARMA spectral estimation,” International Journal of Control, vol. 59, no. 2, pp. 337–356, 1994. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  20. P. M. T. Broersen, “Finite sample criteria for autoregressive order selection,” IEEE Transactions on Signal Processing, vol. 48, no. 12, pp. 3550–3558, 2000. View at Publisher · View at Google Scholar