A data-based algorithm for choosing the window width when estimating the density at a point

Simon Sheather

doi:10.1016/0167-9473(83)90097-X

A data-based algorithm for choosing the window width when estimating the density at a point

Simon Sheather

Research output: Contribution to journal › Article › peer-review

34 Scopus citations

Abstract

When implementing any density estimator, the problem of choosing the smoothing parameter has to be faced. This paper describes a data-based algorithm which chooses the window width for a kernel estimator of the density at a point. The performance of the resulting window width is evaluated using Monte Carlo methods.

Original language	English
Pages (from-to)	229-238
Number of pages	10
Journal	Computational Statistics and Data Analysis
Volume	1
Issue number	C
DOIs	https://doi.org/10.1016/0167-9473(83)90097-X
State	Published - Mar 1983

Keywords

Density estimation
Monte Carlo simulation
Smoothing Parameter

ASJC Scopus subject areas

Statistics and Probability
Computational Mathematics
Computational Theory and Mathematics
Applied Mathematics

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10.1016/0167-9473(83)90097-X

Cite this

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title = "A data-based algorithm for choosing the window width when estimating the density at a point",

abstract = "When implementing any density estimator, the problem of choosing the smoothing parameter has to be faced. This paper describes a data-based algorithm which chooses the window width for a kernel estimator of the density at a point. The performance of the resulting window width is evaluated using Monte Carlo methods.",

keywords = "Density estimation, Monte Carlo simulation, Smoothing Parameter",

author = "Simon Sheather",

year = "1983",

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AU - Sheather, Simon

PY - 1983/3

Y1 - 1983/3

N2 - When implementing any density estimator, the problem of choosing the smoothing parameter has to be faced. This paper describes a data-based algorithm which chooses the window width for a kernel estimator of the density at a point. The performance of the resulting window width is evaluated using Monte Carlo methods.

AB - When implementing any density estimator, the problem of choosing the smoothing parameter has to be faced. This paper describes a data-based algorithm which chooses the window width for a kernel estimator of the density at a point. The performance of the resulting window width is evaluated using Monte Carlo methods.

KW - Density estimation

KW - Monte Carlo simulation

KW - Smoothing Parameter

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JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

IS - C

ER -

A data-based algorithm for choosing the window width when estimating the density at a point

Abstract

Keywords

ASJC Scopus subject areas

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