Kernel smoothing of periodograms under Kullback-Leibler discrepancy

Signal Processing

Volumn 84, Issue 7, 2004, Pages 1255-1266

  Hannig, Jan ^a   Lee, Thomas C M ^a  

^a Department of Environmental and Radiological Health Sciences   (United States)

Author keywords

Bandwidth selection; Kullback Leibler discrepancy; Periodogram and log periodogram smoothing; Relative entropy; Spectral density estimation

Indexed keywords

APPROXIMATION THEORY; BANDWIDTH; ENTROPY; MATHEMATICAL MODELS; PROBABILITY DENSITY FUNCTION; PROBLEM SOLVING;

BANDWIDTH SELECTION; KULLBACK LEIBLER DISCREPANCY; PERIODOGRAM AND LOG PERIODOGRAM SMOOTHING; RELATIVE ENTROPY; SPECTRAL DENSITY ESTIMATION;

SIGNAL PROCESSING;

EID: 2642584785     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sigpro.2004.04.007     Document Type: Article

References (22)

1. Beltrão K., Bloomfield P. Determining the bandwidth of a kernel spectrum estimate. J. Time Ser. Anal. 8:1987;21-39.
2. Burnham K.P., Anderson D.R. Model Selection and Inference: A Practical Information-Theoretic Approach. 1998;Springer, New York.
3. Chow G.C. Econometrics. 1983;McGraw-Hill, New York.
4. Fan J., Kreutzberger E. Automatic local smoothing for spectral density estimation. Scand. J. Statist. 25:1998;359-369.
5. Gao H.-Y. Choice of thresholds for wavelet shrinkage estimate of the spectrum. J. Time Ser. Anal. 18:1997;231-251.
6. Gasser T., Müller H.-G., Mammitzsch V. Kernels for nonparametric curve estimation. J. Roy. Statist. Soc. Ser. B. 47:1985;238-252.
7. Hurvich C.M. Data-driven choice of a spectrum estimate: extending the applicability of cross-validation methods. J. Amer. Statist. Assoc. 80:1985;933-940.
8. Hurvich C.M., Beltrão K. Cross-validatory choice of a spectrum estimate and its connections with AIC. J. Time Ser. Anal. 11:1990;121-137.
9. Kooperberg C., Stone C.J., Troung Y.K. Logspline estimation of a possibly mixed spectral distribution. J. Time Ser. Anal. 16:1995;359-388.
10. Lee T.C.M. A simple span selector for periodogram smoothing. Biometrika. 84:1997;965-969.
11. Lee T.C.M. A stabilized bandwidth selection method for kernel smoothing of the periodogram. Signal Processing. 81:2001;419-430.
12. Lee T.C.M. Tree-based wavelet regression for correlated data using the minimum description length principle. Austral. New Zealand, J. Statist. 44:2002;23-39.
13. Lee T.C.M., Wong T.F. Nonparametric log-spectrum estimation using disconnected regression splines and genetic algorithms. Signal Processing. 83:2003;79-90.
14. Linhart H., Zucchini W. Model Selection. 1986;Wiley, New York.
15. Moulin P. Wavelet thresholding techniques for power spectrum estimation. IEEE Trans. Signal Process. 42:1994;3126-3136.
16. Ombao H.C., Raz J.A., Strawderman R.L., von Sachs R. A simple generalised crossvalidation method of span selection for periodogram smoothing. Biometrika. 88:2001;1186-1192.
17. Pawitan Y., O'Sullivan F. Nonparametric spectral density estimation using penalized Whittle likelihood. J. Amer. Statist. Assoc. 89:1994;600-610.
18. Riedel K.S., Sidorenko A. Adaptive smoothing of the log-spectrum with multiple tapering. IEEE Trans. Signal Process. 44:1996;1794-1800.
19. Wahba G. Automatic smoothing of the log periodogram. J. Amer. Statist. Assoc. 75:1980;122-132.
20. Walden A.T., Percival D.B., McCoy E.J. Spectrum estimation by wavelet thresholding of multitaper estimators. IEEE Trans. Signal Process. 46:1998;3153-3165.
21. Wand M.P. A comparison of regression spline smoothing procedures. Comput. Statist. 15:2000;443-462.
22. Wand M.P., Jones M.C. Kernel Smoothing. 1995;Chapman & Hall, London.