A new nonstationarity detector

IEEE Transactions on Signal Processing

Volumn 56, Issue 4, 2008, Pages 1440-1451

  Kay, Steven ^a  

^a University of Rhode Island   (United States)

Author keywords

Signal detection; Spectral analysis

Indexed keywords

MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; RANDOM PROCESSES; REGRESSION ANALYSIS;

AUTOREGRESSIVE PROCESS; NONSTATIONARITY DETECTOR; RAO TEST; STATIONARY GAUSSIAN RANDOM PROCESS;

SIGNAL DETECTION;

EID: 41849124922     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2007.909346     Document Type: Article

References (20)

1. S. Kay, Modern Spectral Estimation: Theory and Application. Englewood Cliffs, NJ: Prentice-Hall, 1988.
2. M. B. Priestley, Spectral Analysis and Time Series. New York: Acadentic Press, 1981.
3. S. Kay, Fundamentals of Statistical Signal Processing: Detection Theory. Upper Saddle River, NJ: Prentice-Hall, 1998.
4. G. E. P. Box and G. M. Jenkins, Time Series Analysis, Forecasting and Control. San Francisco, CA: Holden-Day, 1970.
5. M. Basseville and I. V. Nikiforov, Detection of Abrupt Changes. Englewood Cliffs, NJ: Prentice-Hall, 1993.
6. T. S. Rao, "The fitting of non-stationary time series models with time-dependent parameters," J. R. Statist. Soc., vol. 32, pp. 312-322, 1970.
7. L. A. Liporace, "Linear estimation of nonstationary signals," J. Acoust. Soc. Amer., vol. 58, pp. 1288-1295, 1975.
8. Y. Grenier, "Time-dependent ARMA modeling of nonstationary signals," IEEE Trans. Signal Process., vol. SP-31, no. 4, pp. 899-911, Aug. 1983.
9. M. B. Priestley and T. S. Rao, "A test for non-stationarity of time-series," J. Royal Stat. Soc., vol. 31, pp. 1401-149, 1969.
10. R. F. Brcich and D. R. Iskander, "Testing for stationarity in the frequency domain using a sphericity statistic," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2006, pp. 464-467.
11. T. W. Epps, "Testing that a Gaussian process is stationary," Ann. Statist., vol. 16, pp. 1667-1683, 1988.
12. R. Dalhaus and L. Giraitis, "On the optimal segment length for parameter estimates for locally stationary time series," J. Time Series Analysis, vol. 19, pp. 629-656, 1998.
13. D. Kwiatkowski, P. C. B. Phillips, P. Schmidt, and Y. Shin, "Testing the null hypothesis of stationarity against the alternative of a unit root," J. Economet., vol. 54, pp. 159-178, 1992.
14. J. Rissanen, "Modeling by shortest data description," Automatica vol. 14, pp. 465-471, 1978.
15. S. Kay, "Exponentially embedded families: New approaches to model order estimation," IEEE Trans. Aerosp. Electron., vol. 41, no. 1, pp. 333-345, Jan. 2005.
16. S. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory. Upper Saddle River, NJ: Prentice-Hall, 1993.
17. M. Kendall and A. Stuart, The Advanced Theory of Statistics. New York: Macmillan, 1979, vol. 2.
18. K. C. Sharman and B. Friedlander, "Time-varying autoregressive modeling of a class of nonstationary signals," in Proc. ICASSP. New York:, 1984, pp. 22.2.1-22.2.4.
19. J. J. Rajan and P. J. W. Rayner, "Generalized feature extraction for time-varying autoregressive models," IEEE Trans. Signal Process., vol. 44, no. 10, pp. 2498-2507, Oct. 1996.
20. A. C. Harvey, Forecasting, Structural Time Series Models and the Kalman Filter. New York: Cambridge Univ. Press, 1989.