Fully Nonparametric Probability Density Function Estimation Based on Empirical Mode Decomposition

Abstract:

Empirical Mode Decomposition (EMD) is a non-stationary signal processing method developed recently. It has been applied in many engineering fields. EMD has many similarities with wavelet decomposition. But EMD Decomposition has its own characteristics, especially in accurate rend extracting. Therefore the paper firstly proposes an algorithm of extracting slow-varying trend based on EMD. Then, according to wavelet probability density function estimation method, a new density estimation method based on EMD is presented. The simulations of Gaussian single and mixture model density estimation prove the advantages of the approach with easy computation and more accurate result

• Periodical: Advanced Materials Research (Volume 460)
• Pages: 189-192
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.460.189
• Online since: February 2012
• Authors: Hong Ying Hu, Chun Ming Kan
• Keywords: Empirical Mode Decomposition (EMD), Probability Density Estimation, Wavelet

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