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

Ying Wu; Bin Li; Fan Yan Ping

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

Ying Wu^*, Bin Li, Fan Yan Ping

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	International Conference on Signal Processing Proceedings, ICSP
Publisher	IEEE
Pages	319-322
Number of pages	4
Volume	1
State	Published - Dec 1 1996
Event	Proceedings of the 1996 3rd International Conference on Signal Processing, ICSP'96. Part 1 (of 2) - Beijing, China Duration: Oct 14 1996 → Oct 18 1996

Other

Other	Proceedings of the 1996 3rd International Conference on Signal Processing, ICSP'96. Part 1 (of 2)
City	Beijing, China
Period	10/14/96 → 10/18/96

ASJC Scopus subject areas

Signal Processing

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title = "Nonparametric density estimation using wavelet transformation and scale-space zero-crossing reconstruction",

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.",

author = "Ying Wu and Bin Li and Ping, {Fan Yan}",

year = "1996",

month = dec,

day = "1",

language = "English (US)",

volume = "1",

pages = "319--322",

booktitle = "International Conference on Signal Processing Proceedings, ICSP",

publisher = "IEEE",

note = "Proceedings of the 1996 3rd International Conference on Signal Processing, ICSP'96. Part 1 (of 2) ; Conference date: 14-10-1996 Through 18-10-1996",

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T1 - Nonparametric density estimation using wavelet transformation and scale-space zero-crossing reconstruction

AU - Wu, Ying

AU - Li, Bin

AU - Ping, Fan Yan

PY - 1996/12/1

Y1 - 1996/12/1

N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:0030399108

VL - 1

SP - 319

EP - 322

BT - International Conference on Signal Processing Proceedings, ICSP

PB - IEEE

T2 - Proceedings of the 1996 3rd International Conference on Signal Processing, ICSP'96. Part 1 (of 2)

Y2 - 14 October 1996 through 18 October 1996

ER -