R: Compute a Binned Kernel Functional Estimate

bkfe {KernSmooth} | R Documentation

## Compute a Binned Kernel Functional Estimate

### Description

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

### Usage

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

### Arguments

`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 `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.

### Details

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

### Value

the (scalar) estimated functional.

### Background

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

### References

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.

### Examples

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

[Package KernSmooth version 2.23-13 Index]