Nonparametric density estimation using wavelet transformation and scale-space zero-crossing reconstruction

Ying Wu*, Bin Li, Fan Yan Ping

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Parzen window method requires relatively larger sample set, and the result of estimation is subject to the selection of window width, so the Parzen window method cannot get good estimation to complex distribution that needs multi-resolution. A novel approach, which is based upon wavelet transformation is presented. On the viewpoint of wavelet transformation, the result of Parzen window method is only the smoothing approximation of p.d.f.. Scale space filter technology is used to get rid of the noise produced by smaller sample set. Six simulations show out that this method can successfully solve the dilemma of estimating of p.d.f. by small sample set and complex distribution.

Original languageEnglish (US)
Title of host publicationInternational Conference on Signal Processing Proceedings, ICSP
PublisherIEEE
Pages319-322
Number of pages4
Volume1
StatePublished - Dec 1 1996
EventProceedings of the 1996 3rd International Conference on Signal Processing, ICSP'96. Part 1 (of 2) - Beijing, China
Duration: Oct 14 1996Oct 18 1996

Other

OtherProceedings of the 1996 3rd International Conference on Signal Processing, ICSP'96. Part 1 (of 2)
CityBeijing, China
Period10/14/9610/18/96

ASJC Scopus subject areas

  • Signal Processing

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