EMD of Gaussian white noise: Effects of signal length and sifting number on the statistical properties of intrinsic mode functions

Advances in Adaptive Data Analysis

Volumn 1, Issue 4, 2009, Pages 517-527

  Schlotthauer, Gastón ^a   Torres, María Eugenia ^a   Rufiner, Hugo L ^b   Flandrin, Patrick ^c  

^a UNIVERSIDAD NACIONAL DE ENTRE RÍOS   (Argentina)

^b UNIVERSIDAD NACIONAL DEL LITORAL   (Argentina)

^c UNIVERSITÉ DE LYON   (France)

Author keywords

Empirical Mode Decomposition (EMD); Gaussian white noise; Intrinsic Mode Function (IMF); sifting

Indexed keywords

DATA LENGTH; DENSITY ESTIMATION; EMPIRICAL MODE DECOMPOSITION; EXPERIMENTAL SIMULATIONS; GAUSSIAN WHITE NOISE; INTRINSIC MODE FUNCTIONS; KERNEL SMOOTHING; LARGE DATA; MULTIMODAL DISTRIBUTIONS; NON-GAUSSIAN DISTRIBUTION; NORMALITY TESTS; NUMBER OF ITERATIONS; SIFTING; SIGNAL LENGTH; STATISTICAL PROPERTIES;

GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); SIGNAL PROCESSING; WHITE NOISE;

PROBABILITY DENSITY FUNCTION;

EID: 80052063063     PISSN: 17935369     EISSN: 17937175     Source Type: Journal    
DOI: 10.1142/S1793536909000217     Document Type: Review

References (10)

1. N. E. Huang, Z. Shen, S. R. Long, M. L. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung and H. H. Liu, The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. Roy. Soc. London A 454 (1998) 903-995. (Pubitemid 128733183)
2. P. Flandrin, G. Rilling and P. Gonçalvès, Empirical mode decomposition as a filter bank, IEEE Signal Process. Lett. 11 (2004) 112-114.
3. P. Flandrin and P. Gonçalvès, Empirical mode decomposition as data-driven waveletlike expansions, Int. J. Wavelets Multiresolution Inform. Process. 2 (2004) 477-496.
4. Z. Wu and N. E. Huang, A study of the characteristics of white noise using the empirical mode decomposition method, Proc. Roy. Soc. London A 460 (2004) 1597-1611. (Pubitemid 38703409)
5. Z. Wu and N. E. Huang, Statistical significance test of intrinsic mode functions, in Hilbert-Huang Transform and its Applications, in Interdisciplinary Mathematical Science, Vol. 5 (World Scientific, Singapore, 2005), pp. 107-127.
6. C. M. Jarque and A. L. Bera, A test for normality of observations and regression residuals, Int. Stat. Rev. 55 (1987) 163-172.
7. G. Rilling, P. Flandrin and P. Gonçalvès, On Empirical Mode Decomposition and its algorithms, in IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, NSIP-03, Grado (I), June 2003.
8. http://perso.ens-lyon.fr/patrick.flandrin/emd.html
9. A. W. Bowman and A. Azzalini, Applied smoothing techniques for data analysis, in The Kernel Approach with S-plus Illustrations (Oxford University Press, New York, 1997).
10. Z. Wu and N. E. Huang, Ensemble empirical mode decomposition: A noise-assisted data analysis method, Adv. Adapt. Data Anal. 1 (1) (2009) 1-41.