A confidence limit for the empirical mode decomposition and Hilbert spectral analysis

  title={A confidence limit for the empirical mode decomposition and Hilbert spectral analysis},
  author={Norden E. Huang and Man-li C. Wu and Steven R. Long and Samuel S. P. Shen and Wendong Qu and Per Gloersen and Kuang-Lung Fan},
  journal={Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences},
  pages={2317 - 2345}
  • N. Huang, Man-li C. Wu, Kuang-Lung Fan
  • Published 8 September 2003
  • Mathematics
  • Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences
The confidence limit is a standard measure of the accuracy of the result in any statistical analysis. Most of the confidence limits are derived as follows. The data are first divided into subsections and then, under the ergodic assumption, the temporal mean is substituted for the ensemble mean. Next, the confidence limit is defined as a range of standard deviations from this mean. However, such a confidence limit is valid only for linear and stationary processes. Furthermore, in order for the… 


The Hilbert–Huang transform (HHT) is an empirically based data-analysis method. Its basis of expansion is adaptive, so that it can produce physically meaningful representations of data from nonlinear


This chapter is an introduction to the basic method of the Hilbert-Huang transform, followed by brief descriptions of the recent developments relating to the normalized Hilbert transform, a confidence limit for the Hilbert spectrum, and a statistical significance test for the intrinsic mode function (IMF).

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The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis

  • N. HuangZheng Shen Henry H. Liu
  • Mathematics
    Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences
  • 1998
A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the ‘empirical mode decomposition’ method with which any complicated data set can be

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