ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R

Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing. Currently it contains functionality for kernel density estimation and kernel discriminant analysis. It is a comprehensive package for bandwidth...

Full description

Bibliographic Details
Main Author: Tarn Duong
Format: Article
Language:English
Published: Foundation for Open Access Statistics 2007-09-01
Series:Journal of Statistical Software
Subjects:
Online Access:http://www.jstatsoft.org/v21/i07/paper
id doaj-6a97d9cdfe7a4ff8bcc7a471c80a89be
record_format Article
spelling doaj-6a97d9cdfe7a4ff8bcc7a471c80a89be2020-11-24T22:56:09ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602007-09-01217ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in RTarn DuongKernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing. Currently it contains functionality for kernel density estimation and kernel discriminant analysis. It is a comprehensive package for bandwidth matrix selection, implementing a wide range of data-driven diagonal and unconstrained bandwidth selectors.http://www.jstatsoft.org/v21/i07/paperbandwidth selectiondata-drivennon-parametric smoothing
collection DOAJ
language English
format Article
sources DOAJ
author Tarn Duong
spellingShingle Tarn Duong
ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R
Journal of Statistical Software
bandwidth selection
data-driven
non-parametric smoothing
author_facet Tarn Duong
author_sort Tarn Duong
title ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R
title_short ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R
title_full ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R
title_fullStr ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R
title_full_unstemmed ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R
title_sort ks: kernel density estimation and kernel discriminant analysis for multivariate data in r
publisher Foundation for Open Access Statistics
series Journal of Statistical Software
issn 1548-7660
publishDate 2007-09-01
description Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing. Currently it contains functionality for kernel density estimation and kernel discriminant analysis. It is a comprehensive package for bandwidth matrix selection, implementing a wide range of data-driven diagonal and unconstrained bandwidth selectors.
topic bandwidth selection
data-driven
non-parametric smoothing
url http://www.jstatsoft.org/v21/i07/paper
work_keys_str_mv AT tarnduong kskerneldensityestimationandkerneldiscriminantanalysisformultivariatedatainr
_version_ 1725654560005947392