Density estimation

Simon J. Sheather

doi:10.1214/088342304000000297

Density estimation

Simon J. Sheather

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

405 Citations (SciVal)

Abstract

This paper provides a practical description of density estimation based on kernel methods. An important aim is to encourage practicing statisticians to apply these methods to data. As such, reference is made to implementations of these methods in R, S-PLUS and SAS.

Original language	English
Pages (from-to)	588-597
Number of pages	10
Journal	Statistical Science
Volume	19
Issue number	4
DOIs	https://doi.org/10.1214/088342304000000297
State	Published - Nov 2004

Keywords

Bandwidth selection
Data sharpening
Kernel density estimation
Local likelihood density estimates

ASJC Scopus subject areas

Statistics and Probability
Mathematics (all)
Statistics, Probability and Uncertainty

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10.1214/088342304000000297

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Density estimation

Abstract

Keywords

ASJC Scopus subject areas

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