ks: Kernel density estimation and kernel discriminant analysis for multivariate data in R

被引：433

作者：

Duong, Tarn ^{[1
]}

机构：

[1] Inst Pasteur, Unit Anal Images Quantitat, F-75015 Paris, France

来源：

JOURNAL OF STATISTICAL SOFTWARE | 2007年 / 21卷 / 07期

关键词：

bandwidth selection; data-driven; non-parametric smoothing;

D O I：

10.18637/jss.v021.i07

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：1 / 16

页数：16

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