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 = 401L, range.x, binned = FALSE,
truncate = TRUE)x |
numeric 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. Must be supplied. |
gridsize |
the number of equally-spaced points over which binning is performed. |
range.x |
vector containing the minimum and maximum values of |
binned |
logical flag: if |
truncate |
logical flag: if |
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 (scalar) 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)
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