hnorm: Normal optimal choice of smoothing parameter in density estimation
Description
This functions evaluates the smoothing parameter which is asymptotically
optimal for estimating a density function when the underlying distribution
is Normal. Data in one, two or three dimensions can be handled.
Usage
hnorm(x, weights)
Value
the value of the asymptotically optimal smoothing parameter for Normal case.
Arguments
x
a vector, or matrix with two or three columns, containing the data.
weights
an optional vector of integer values
which allows the kernel functions over the observations to take
different weights when they are averaged to produce a density estimate. This
is useful, in particular, for censored data and to construct an estimate
from binned data.
Details
See Section 2.4.2 of the reference below.
References
Bowman, A.W. and Azzalini, A. (1997).
Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations.
Oxford University Press, Oxford.