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ks (version 1.7.0)

Hscv: Smoothed cross-validation (SCV) bandwidth selector

Description

SCV bandwidth for 1- to 6-dimensional data.

Usage

Hscv(x, pre="sphere", pilot="samse", Hstart, binned=TRUE, bgridsize,
     amise=FALSE, kfold=1)
Hscv.diag(x, pre="scale", Hstart, binned=FALSE, bgridsize, 
     amise=FALSE, kfold=1)
hscv(x, nstage=2, binned=TRUE, bgridsize, plot=FALSE)

Arguments

x
vector or matrix of data values
pre
"scale" = pre-scaling, "sphere" = pre-sphering
pilot
"amse" = AMSE pilot bandwidths, "samse" = single SAMSE pilot bandwidth, "unconstr" = unconstrained pilot bandwidth matrix
Hstart
initial bandwidth matrix, used in numerical optimisation
binned
flag for binned kernel estimation. Default is FALSE.
bgridsize
vector of binning grid sizes
amise
flag to return the minimal scaled SCV value. Default is FALSE.
kfold
value for k-fold bandwidth selection. See details below.
nstage
number of stages in the SCV bandwidth selector (1 or 2) (1-d only)
plot
flag to display plot of SCV(h) vs h (1-d only). Default is FALSE.

Value

  • SCV bandwidth. If amise=TRUE then the minimal scaled SCV value is returned too.

Details

hsv is the univariate SCV selector of Jones, Marron & Park (1991). Hscv is a multivariate generalisation of this, see Duong & Hazelton (2005). Use Hscv for full bandwidth matrices and Hscv.diag for diagonal bandwidth matrices.

For d = 1, the selector hscv is not always stable for large sample sizes with binning. Examine the plot from hscv(, plot=TRUE) to determine the appropriate smoothness of the SCV function. Any non-smoothness is due to the discretised nature of binned estimation. For details about the advanced options for amise, binned, Hstart, kfold, see Hpi.

References

Jones, M.C., Marron, J.S. & Park, B.U. (1991) A simple root n bandwidth selector. Annals of Statistics 19, 1919-1932.

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

See Also

Hlscv, Hbcv, Hpi

Examples

Run this code
data(unicef)
Hscv(unicef)
Hscv.diag(unicef, binned=TRUE)
hscv(unicef[,1])

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