ks-package.rd

r软件 另一款可以计算核估计的软件包 需安装r软件

\name{ks}
\alias{ks}
\docType{package}
\title{
ks
}
\description{
  Kernel density estimation and kernel discriminant analysis for
  data from 1- to 6-dimensions, with display functions.
  
}
\details{
  There are three main types of functions in this package:
  (a) computing bandwidth
  selectors, (b) computing kernel estimators and (c) displaying kernel estimators.

  For the bandwidth matrix selectors, there are several varieties:\cr
  (i) plug-in \code{\link{hpi}} (1-d); 
  \code{\link{Hpi}}, \code{\link{Hpi.diag}} (2- to 6-d) \cr
  (ii) least squares (or unbiased) cross validation (LSCV or UCV)
  \code{\link{Hlscv}}, \code{\link{Hlscv.diag}} (2- to 6-d) \cr
  (iii) biased cross validation (BCV) 
  \code{\link{Hbcv}}, \code{\link{Hbcv.diag}} (2- to 6-d) \cr
  (iv) smoothed cross validation (SCV) \code{\link{hscv}} (1-d);
  \code{\link{Hscv}}, \code{\link{Hscv.diag}} (2- to 6-d) \cr
  (v) normal scale selectors \code{\link{hmise.mixt}},
  \code{\link{hamise.mixt}} (1-d); and \code{\link{Hmise.mixt}},
  \code{\link{Hamise.mixt}} (2- to 6-d).
  
  
  For kernel density estimation, the main function is
  \code{\link{kde}}. For kernel discriminant analysis,
  it's \code{\link{kda.kde}}.

  For display, \code{plot} via (\code{\link{plot.kde}} and
  \code{\link{plot.kda.kde}}) sends to a graphics window 
  the results of density estimation or discriminant analysis.
  
  Binned kernel estimation is available for d = 1, 2, 3, 4.
  
  For an overview of this package with 2-d density estimation, see 
  \code{vignette("kde")}. 
}
\author{
  Tarn Duong for most of the package. 
  Matt Wand for the binned estimation, univariate plug-in selector
  and density derivative estimator code.
  Jose E. Chac\'on for the unconstrained pilot
  functional estimation and (A)MISE-optimal selectors for normal mixture
  densities code.   
}
\references{
  Bowman, A. \& Azzalini, A. (1997) \emph{Applied Smoothing Techniques
    for Data Analysis}. Oxford University Press. Oxford.

  Chac\'on, J.E. \& Duong, T. (2008) Multivariate plug-in bandwidth
  selection with unconstrained pilot matrices. \emph{Submitted.}
  
  Duong, T. (2004) \emph{Bandwidth Matrices for Multivariate Kernel Density 
    Estimation.} Ph.D. Thesis. University of Western Australia. 
 
  Duong, T. \& Hazelton, M.L. (2003) Plug-in bandwidth matrices for
    bivariate kernel density estimation. \emph{Journal of Nonparametric
  Statistics}, \bold{15}, 17-30. 
  
  Duong, T. \& Hazelton, M.L. (2005) Cross-validation bandwidth  
    matrices for multivariate kernel density
	estimation. \emph{Scandinavian Journal of Statistics}, \bold{32},
	485-506. 

  Sain, S.R., Baggerly, K.A. \& Scott, D.W. (1994)
  Cross-validation of multivariate densities. \emph{Journal of the
  American Statistical Association}. \bold{82}, 1131-1146.

  Scott, D.W. (1992) \emph{Multivariate Density Estimation: Theory,
    Practice, and Visualization}. John Wiley \& Sons. New York.

  Silverman, B. (1986) \emph{Density Estimation for Statistics and
  Data Analysis}. Chapman \& Hall/CRC. London.

  Simonoff, J. S. (1996) \emph{Smoothing Methods in Statistics}.
  Springer-Verlag. New York.

  Wand, M.P. \& Jones, M.C. (1994) Multivariate plugin bandwidth
    selection. \emph{Computational Statistics}, \bold{9}, 97-116.
  
  Wand, M.P. \& Jones, M.C. (1995) \emph{Kernel Smoothing}. Chapman \&
  Hall/CRC. London.
}

\keyword{ package }

\seealso{
\code{\link[sm:sm-package]{sm}}, \code{\link[KernSmooth]{KernSmooth}}
}