Five separate bias contributions in time series models for equidistantly resampled irregular data

IEEE Transactions on Instrumentation and Measurement

Volumn 58, Issue 5, 2009, Pages 1370-1379

  Broersen, Piet M T ^a  

^a DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

Autoregressive (AR) models; Nearest neighbor (NN) resampling; Slotting; Spectral analysis; Time series; Uneven sampling

Indexed keywords

ALIASING; AUTO CORRELATION FUNCTIONS; AUTOREGRESSIVE (AR) MODELS; DATA SETS; FREQUENCY DOMAINS; INSTANTANEOUS AMPLITUDES; IRREGULAR DATUM; MAXIMUM-LIKELIHOOD; MODEL ORDERS; NEAREST-NEIGHBOR (NN) RESAMPLING; SAMPLE SIZES; SPECTRAL ANALYSIS; SPECTRAL ESTIMATES; TIME-SERIES MODELS; UNEVEN SAMPLING;

MAXIMUM LIKELIHOOD ESTIMATION; PROBABILITY; REGRESSION ANALYSIS; SLOTTING; SPECTRUM ANALYSIS; SPECTRUM ANALYZERS; TIME SERIES;

TIME SERIES ANALYSIS;

EID: 65549097588     PISSN: 00189456     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIM.2009.2012928     Document Type: Conference Paper

References (27)

1. J. Mateo and P. Laguna, "Improved heart rate variability signal analysis from the beat occurrence times according to the IPFM model," IEEE Trans. Biomed. Eng., vol. 47, no. 8, pp. 985-996, Aug. 2000.
2. C. Thiebaut and S. Roques, "Time-scale and time-frequency analyses of irregularly sampled astronomical time series," EURASIP J. Appl. Signal Process., vol. 2005, no. 15, pp. 2486-2499, 2005.
3. L. H. Benedict, H. Nobach, and C. Tropea, "Estimation of turbulent velocity spectra from laser Doppler data," Meas. Sci. Technol., vol. 11, no. 8, pp. 1089-1104, Aug. 2000.
4. M. J. Tummers and D. M. Passchier, "Spectral estimation using a variable window and the slotting technique with local normalization," Meas. Sci. Technol., vol. 7, no. 11, pp. 1541-1546, Nov. 1996.
5. R. H. Jones, "Fitting a continuous time autoregression to discrete data," in Applied Time Series Analysis II, D. F. Findley, Ed. New York: Academic, 1981, pp. 651-682.
6. P. M. T. Broersen and R. Bos, "Estimating time-series models from irregularly spaced data," IEEE Trans. Instrum. Meas., vol. 55, no. 4, pp. 1124-1131, Aug. 2006.
7. G. C. Goodwin, J. I. Yuz, and J. C. Agüero, "Relative error issues in sampled data models," in Proc. 17th World Congr. IFAC, Seoul, Korea, 2008, pp. 5047-5052.
8. L. Ljung and A. Wills, "Issues in sampling and estimating continuous-time models with stochastic disturbances," in Proc. 17th World Congr. IFAC, Seoul, Korea, 2008, pp. 14 360-14 365.
9. R. Pintelon, J. Schouten, and P. Guillaume, "Continuous-time noise modeling from sampled data," IEEE Trans. Instrum. Meas., vol. 55, no. 6, pp. 2253-2258, Dec. 2006.
10. R. J. Adrian and C. S. Yao, "Power spectra of fluid velocities measured by laser Doppler velocimetry," Exp. Fluids, vol. 5, no. 1, pp. 17-28, Jan. 1987.
11. P. M. T. Broersen, "Time series models for spectral analysis of irregular data far beyond the mean data rate," Meas. Sci. Technol. vol. 19, no. 1, pp. 015 103-1-015 103-14, Jan. 2008.
12. P. M. T. Broersen, S. de Waele, and R. Bos, "Autoregressive spectral analysis when observations are missing," Automatica, vol. 40, no. 9, pp. 1495-1504, Sep. 2004.
13. R. H. Jones, "Maximum likelihood fitting of ARMA models to time series with missing observations," Technometr., vol. 22, no. 3, pp. 389-395, Aug. 1980.
14. M. J. Tummers and D. M. Passchier, "Spectral analysis of biased LDA data," Meas. Sci. Technol., vol. 12, no. 10, pp. 1641-1650, Oct. 2001.
15. D. K. McLaughlin andW. G. Tiedeman, "Biasing correction for individual realization of laser anemometer measurements in turbulent flows," Phys. Fluids, vol. 16, no. 12, pp. 2082-2088, 1973.
16. T. J. McDougall, "Bias correction for individual realisation LDA measurements," J. Phys. E, Sci. Instrum., vol. 13, no. 1, pp. 53-60, Jan. 1980.
17. P. M. T. Broersen, "Bias contributions in time series models for resampled irregular data," in Proc. I2MTC, Victoria, BC, Canada, 2008, pp. 882-889.
18. M. B. Priestley, Spectral Analysis and Time Series. London, U.K.: Academic, 1981.
19. P. M. T. Broersen, Automatic Autocorrelation and Spectral Analysis London, U.K.: Springer-Verlag, 2006.
20. G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control. San Francisco, CA: Holden-Day, 1976.
21. E. J. Hannan and M. F. Deistler, The Statistical Theory of Linear Systems. New York: Wiley, 1988.
22. D. Britz and R. A. Antonia, "A comparison of computing power spectra of LDA signals," Meas. Sci. Technol., vol. 7, no. 7, pp. 1042-1053, Jul. 1996.
23. P. M. T. Broersen, ARMAsel for Irregular or Missing Data, Matlab toolbox, 2001. [Online]. Available: Www.mathworks.com/matlabcentral/ fileexchange/?term=armasa
24. L. Barford, "Filtering of randomly sampled time-stamped measurements," IEEE Trans. Instrum. Meas., vol. 57, no. 2, pp. 222-227, Feb. 2008.
25. P. M. T. Broersen, "Application of time series models to the spectral analysis of irregular turbulence data," in Proc. I2MTC, Victoria, BC, Canada, 2008, pp. 33-38.
26. H. Nobach, LDA benchmark generator III, 2001. [Online]. Available: http://www.nambis.de/benchmark
27. W. K. Harteveld, R. F.Mudde, and H. E. A. van den Akker, "Estimation of turbulence power spectra for bubbly flows from laser Doppler anemometry signals," Chem. Eng. Sci., vol. 60, no. 22, pp. 6160-6168, 2005.