Nonparametric density estimation with adaptive varying window size

V. Katkovnik; I. Shmulevich

doi:10.1117/12.413890

Nonparametric density estimation with adaptive varying window size

V. Katkovnik
, I. Shmulevich

Research output: Contribution to journal › Conference article › peer-review

6 Scopus citations

Abstract

We propose a new method of kernel density estimation with a varying adaptive window width. This method is different from traditional ones in two aspects. First, we use symmetric as well as nonsymmetric left and right kernels with discontinuities and show that the fusion of these estimates results in accuracy improvement. Second, we develop estimates with adaptive varying window widths based on the so-called intersection of confidence intervals (ICI) rule. Several examples of the proposed method are given for different types of densities and the quality of the adaptive density estimate is assessed by means of numerical simulations.

Original language	English
Pages (from-to)	141-150
Number of pages	10
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	4170
DOIs	https://doi.org/10.1117/12.413890
State	Published - 2001
Externally published	Yes
Event	Image and Signal Processing for Remote Sensing VI - Barcelona, Spain Duration: Sep 27 2000 → Sep 29 2000

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@article{830b7f10dbea426a96c85f886ac680bb,

title = "Nonparametric density estimation with adaptive varying window size",

abstract = "We propose a new method of kernel density estimation with a varying adaptive window width. This method is different from traditional ones in two aspects. First, we use symmetric as well as nonsymmetric left and right kernels with discontinuities and show that the fusion of these estimates results in accuracy improvement. Second, we develop estimates with adaptive varying window widths based on the so-called intersection of confidence intervals (ICI) rule. Several examples of the proposed method are given for different types of densities and the quality of the adaptive density estimate is assessed by means of numerical simulations.",

author = "V. Katkovnik and I. Shmulevich",

note = "Funding Information: The authors duly acknowledge financial assistance from the Department of Science and Technology (DST), New Delhi, as well as the University Grant Commission (UGC), New Delhi, under UPE and CPEPA programs. The authors state no conflict of interest in doing this research.; Image and Signal Processing for Remote Sensing VI ; Conference date: 27-09-2000 Through 29-09-2000",

year = "2001",

doi = "10.1117/12.413890",

language = "English",

volume = "4170",

pages = "141--150",

journal = "Proceedings of SPIE - The International Society for Optical Engineering",

issn = "0277-786X",

publisher = "SPIE",

}

TY - JOUR

T1 - Nonparametric density estimation with adaptive varying window size

AU - Katkovnik, V.

AU - Shmulevich, I.

N1 - Funding Information: The authors duly acknowledge financial assistance from the Department of Science and Technology (DST), New Delhi, as well as the University Grant Commission (UGC), New Delhi, under UPE and CPEPA programs. The authors state no conflict of interest in doing this research.

PY - 2001

Y1 - 2001

N2 - We propose a new method of kernel density estimation with a varying adaptive window width. This method is different from traditional ones in two aspects. First, we use symmetric as well as nonsymmetric left and right kernels with discontinuities and show that the fusion of these estimates results in accuracy improvement. Second, we develop estimates with adaptive varying window widths based on the so-called intersection of confidence intervals (ICI) rule. Several examples of the proposed method are given for different types of densities and the quality of the adaptive density estimate is assessed by means of numerical simulations.

AB - We propose a new method of kernel density estimation with a varying adaptive window width. This method is different from traditional ones in two aspects. First, we use symmetric as well as nonsymmetric left and right kernels with discontinuities and show that the fusion of these estimates results in accuracy improvement. Second, we develop estimates with adaptive varying window widths based on the so-called intersection of confidence intervals (ICI) rule. Several examples of the proposed method are given for different types of densities and the quality of the adaptive density estimate is assessed by means of numerical simulations.

UR - https://www.scopus.com/pages/publications/0035050854

U2 - 10.1117/12.413890

DO - 10.1117/12.413890

M3 - Conference article

AN - SCOPUS:0035050854

SN - 0277-786X

VL - 4170

SP - 141

EP - 150

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

T2 - Image and Signal Processing for Remote Sensing VI

Y2 - 27 September 2000 through 29 September 2000

ER -

Nonparametric density estimation with adaptive varying window size

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