ks {ks}R Documentation

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:
(i) plug-in hpi (1-d); Hpi, Hpi.diag (2- to 6-d)
(ii) least squares (or unbiased) cross validation (LSCV or UCV) Hlscv, Hlscv.diag (2- to 6-d)
(iii) biased cross validation (BCV) Hbcv, Hbcv.diag (2- to 6-d)
(iv) smoothed cross validation (SCV) hscv (1-d); Hscv, Hscv.diag (2- to 6-d)
(v) normal scale selectors hmise.mixt, hamise.mixt (1-d); and Hmise.mixt, Hamise.mixt (2- to 6-d).

For kernel density estimation, the main function is kde. For kernel discriminant analysis, it's kda.kde.

For display, plot via (plot.kde and 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 vignette("kde").

Author(s)

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) 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. Submitted.

Duong, T. (2004) 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. Journal of Nonparametric Statistics, 15, 17-30.

Duong, T. & Hazelton, M.L. (2005) Cross-validation bandwidth matrices for multivariate kernel density estimation. Scandinavian Journal of Statistics, 32, 485-506.

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

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

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

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

Wand, M.P. & Jones, M.C. (1994) Multivariate plugin bandwidth selection. Computational Statistics, 9, 97-116.

Wand, M.P. & Jones, M.C. (1995) Kernel Smoothing. Chapman & Hall/CRC. London.

See Also

sm, KernSmooth


[Package ks version 1.6.2 Index]