bkfe {KernSmooth}R Documentation

Compute a Binned Kernel Functional Estimate


Returns an estimate of a binned approximation to the kernel estimate of the specified density functional. The kernel is the standard normal density.


bkfe(x, drv, bandwidth, gridsize = 401, range.x, binned = FALSE,
     truncate = TRUE)


x vector of observations from the distribution whose density is to be estimated. Missing values are not allowed.
drv order of derivative in the density functional. Must be a non-negative even integer.
bandwidth the kernel bandwidth smoothing parameter.
gridsize the number of equally-spaced points over which binning is performed.
range.x vector containing the minimum and maximum values of x at which to compute the estimate. The default is the minimum and maximum data values, extended by the support of the kernel.
binned logical flag: if TRUE, then x and y are taken to be grid counts rather than raw data.
truncate logical flag: if TRUE, data with x values outside the range specified by range.x are ignored.


The density functional of order drv is the integral of the product of the density and its drvth derivative. The kernel estimates of such quantities are computed using a binned implementation, and the kernel is the standard normal density.


the estimated functional.


Estimates of this type were proposed by Sheather and Jones (1991).


Sheather, S. J. and Jones, M. C. (1991). A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society, Series B, 53, 683–690.

Wand, M. P. and Jones, M. C. (1995). Kernel Smoothing. Chapman and Hall, London.


data(geyser, package="MASS")
x <- geyser$duration
est <- bkfe(x, drv=4, bandwidth=0.3)

[Package KernSmooth version 2.22-20 Index]