ks (version 1.4.4)

ks: ks

Description

Kernel density estimation and kernel discriminant analysis for multivariate data (1- to 6-dimensions) with display functions.

Details

There are three main types of functions in this package: (a) bandwidth selectors, (b) kernel estimators and (c) display.

(a) For the bandwidth matrix selectors, there are several varieties: (i) plug-in Hpi, (ii) least squares (or unbiased) cross validation (LSCV or UCV) Hlscv, (iii) biased cross validation (BCV) Hbcv and (iv) smoothed cross validation (SCV) Hscv. Scalar bandwidth selectors are not provided - see sm or KernSmooth packages.

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

(c) For display, versions of plot send to a graphics window the results of density estimation or discriminant analysis.

References

Bowman, A. & Azzalini, A. (1997) Applied Smoothing Techniques for Data Analysis. Oxford University Press. Oxford. 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. Mew York. 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