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

Simon J. Sheather

doi:10.1016/0167-9473(86)90026-5

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

Simon J. Sheather

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

55 Scopus citations

Abstract

Sheather [3] proposed a data-based algorithm for choosing the window width when estimating the density at a point by a kernel estimator. Unfortunately, for some densities the windows widths produced by the algorithm have considerable bias. More importantly the resulting density estimator is not scale invariant. In this note we show how a modification to the algorithm overcomes these two problems.

Original language	English
Pages (from-to)	61-65
Number of pages	5
Journal	Computational Statistics and Data Analysis
Volume	4
Issue number	1
DOIs	https://doi.org/10.1016/0167-9473(86)90026-5
State	Published - Jun 1986

Keywords

Kernel density estimation
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(86)90026-5

Cite this

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

abstract = "Sheather [3] proposed a data-based algorithm for choosing the window width when estimating the density at a point by a kernel estimator. Unfortunately, for some densities the windows widths produced by the algorithm have considerable bias. More importantly the resulting density estimator is not scale invariant. In this note we show how a modification to the algorithm overcomes these two problems.",

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N2 - Sheather [3] proposed a data-based algorithm for choosing the window width when estimating the density at a point by a kernel estimator. Unfortunately, for some densities the windows widths produced by the algorithm have considerable bias. More importantly the resulting density estimator is not scale invariant. In this note we show how a modification to the algorithm overcomes these two problems.

AB - Sheather [3] proposed a data-based algorithm for choosing the window width when estimating the density at a point by a kernel estimator. Unfortunately, for some densities the windows widths produced by the algorithm have considerable bias. More importantly the resulting density estimator is not scale invariant. In this note we show how a modification to the algorithm overcomes these two problems.

KW - Kernel density estimation

KW - Smoothing parameter

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VL - 4

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JO - Computational Statistics and Data Analysis

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IS - 1

ER -

An improved 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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